Matching using agent/caller sensitivity to performance

ABSTRACT

A method, system and program product, the method comprising: obtaining for each call in one set of calls a respective pattern representing multiple different respective data fields; obtaining performance data for the respective patterns of the calls; obtaining performance data for the respective agents; determining agent performance sensitivity to call pattern performance for agents in a set of agents comprising the agent performance data correlated to call performance data for the calls the agent handles; and matching a respective one of the agents from the set of agents to one of the calls based at least in part on the performance data for the respective pattern of the one call and on the agent sensitivity to call performance for the respective one agent of the set of agents.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/359,649, filed Mar. 20, 2019, now U.S. Pat. No. 10,419,616, which isa continuation of U.S. patent application Ser. No. 15/987,287, filed May23, 2018, now U.S. Pat. No. 10,244,117, which is a continuation of U.S.patent application Ser. No. 15/193,673, filed Jun. 27, 2016, now U.S.Pat. No. 10,027,811, which is a continuation of U.S. patent applicationSer. No. 14/035,522, filed Sep. 24, 2013, now U.S. Pat. No. 9,462,127,which claims priority from Provisional U.S. application Ser. No.61/705,040 filed Sep. 24, 2012; and U.S. patent application Ser. No.15/987,287 is also a continuation of U.S. patent application Ser. No.15/193,687, filed Jun. 27, 2016, now U.S. Pat. No. 10,027,812, which isa continuation of U.S. patent application Ser. No. 14/035,522, filedSep. 24, 2013, now U.S. Pat. No. 9,462,127, which claims priority fromProvisional U.S. Application No. 61/705,040, filed Sep. 24, 2012, eachof which is hereby incorporated by reference in its entirety as if fullyset forth herein.

BACKGROUND OF THE DISCLOSURE

The present disclosure relates to the field of routing phone calls andother telecommunications in a contact center system.

The typical contact center consists of a number of human agents, witheach assigned to a telecommunication device, such as a phone or acomputer for conducting email or Internet chat sessions, that isconnected to a central switch. Using these devices, the agents aregenerally used to provide sales, customer service, or technical supportto the customers or prospective customers of a contact center or acontact center's clients.

Typically, a contact center or client will advertise to its customers,prospective customers, or other third parties a number of differentcontact numbers or addresses for a particular service, such as forbilling questions or for technical support. The customers, prospectivecustomers, or third parties seeking a particular service will then usethis contact information, and the incoming caller will be routed at oneor more routing points to a human agent at a contact center who canprovide the appropriate service. Contact centers that respond to suchincoming contacts are referred to as “inbound contact centers.”

Similarly, a contact center can make outgoing contacts to current orprospective customers or third parties. Such contacts may be made toencourage sales of a product, provide technical support or billinginformation, survey consumer preferences, or to assist in collectingdebts. Contact centers that make such outgoing contacts are referred toas “outbound contact centers.”

In both inbound contact centers and outbound contact centers, theindividuals (such as customers, prospective customers, surveyparticipants, or other third parties) that interact with contact centeragents over the telephone are referred to in this application as a“caller.” The individuals acquired by the contact center to interactwith callers are referred to in this application as an “agent.”

A piece of hardware for any contact center operation is the switchsystem that connects callers to agents. In an inbound contact center,these switches route incoming callers to a particular agent in a contactcenter, or, if multiple contact centers are deployed, to a particularcontact center for further routing. In an outbound contact centeremploying telephone devices, dialers are typically employed in additionto a switch system. The dialer is used to automatically dial a phonenumber from a list of phone numbers, and to determine whether a livecaller has been reached from the phone number called (as opposed toobtaining no answer, a busy signal, an error message, or an answeringmachine). When the dialer obtains a live caller, the switch systemroutes the caller to a particular agent in the contact center.

Contact routing in an inbound contact center is a process that isgenerally structured to connect callers to agents that have been idlefor the longest period of time. In the case of an inbound caller whereonly one agent may be available, that agent is generally selected forthe caller without further analysis. In another example, if there areeight agents at a contact center, and seven are occupied with contacts,the switch will generally route the inbound caller to the one agent thatis available. If all eight agents are occupied with contacts, the switchwill typically put the contact on hold and then route it to the nextagent that becomes available. More generally, the contact center willset up a queue of incoming callers and preferentially route thelongest-waiting callers to the agents that become available over time.Such a pattern of routing contacts to either the first available agentor the longest-waiting agent is referred to as “round-robin” contactrouting. In round robin contact routing, eventual matches andconnections between a caller and an agent are essentially random.

In an outbound contact center environment using telephone devices, thecontact center or its agents are typically provided a “lead list”comprising a list of telephone numbers to be contacted to attempt somesolicitation effort, such as attempting to sell a product or conduct asurvey. The lead list can be a comprehensive list for all contactcenters, one contact center, all agents, or a sub-list for a particularagent or group of agents (in any such case, the list is generallyreferred to in this application as a “lead list”). After receiving alead list, a dialer or the agents themselves will typically call throughthe lead list in numerical order, obtain a live caller, and conduct thesolicitation effort. In using this standard process, the eventualmatches and connections between a caller and an agent are essentiallyrandom.

There is a need for improving on the available mechanisms for matchingand connecting a caller to an agent. The present disclosure reflectsthis.

BRIEF SUMMARY OF THE DISCLOSURE

In embodiments, a method may comprise: obtaining for each call in oneset of calls, by the one or more computers, a respective patternrepresenting one or multiple different respective data fields;obtaining, by the one or more computers, performance data for therespective patterns of the calls; obtaining, by the one or morecomputers, performance data for respective agents in a set of agents;determining, by the one or more computers, agent performance sensitivityto call pattern performance for agents in the set of agents comprisingthe agent performance data correlated to the call performance data forthe calls the agent has handled; and matching, by the one or morecomputers, a respective one of the agents from the set of agents to oneof the calls based at least in part on the performance data for therespective pattern of the one call and on the agent sensitivity to callperformance for the respective one agent of the set of agents.

In embodiments, the determining agent performance sensitivity to callpattern performance for agents in a set of agents may comprisecorrelating agent performance to call performance data for the calls theagent has handled in a data set.

In embodiments, the method may further comprise: percentiling orranking, by the one or more computers, the respective patterns for theset of calls based at least in part on their respective performancedata; percentiling or ranking, by the one or more computers, the agentsin the set of agents based at least in part on the agent performancesensitivity to call pattern performance, wherein the matching is basedat least in part on the percentile or ranking of the respective patternof the one call and the percentile or ranking by agent performancesensitivity to call pattern performance of the one agent.

In embodiments, the pattern may be abstracted in whole or in part, sothat a meaning for the field data in the pattern is not known by thesystem.

In embodiments, the performance data may be based on at least oneselected from the group of sales, retention of the caller in a program,call handle time, customer satisfaction, revenue, first call resolution,units sold, and transaction points.

In embodiments, the method may further comprise: matching, by the one ormore computers, a different set of calls to agents using a differentmatching algorithm; comparing, performance data from call-agent matchesof the different set of calls using the different matching algorithmwith performance data for the one set of calls matched based at least inpart on the agent sensitivity to call performance; and generating, bythe one or more computers, a report or display data for the performancecomparing results from using the matching algorithm based at least inpart on agent sensitivity to call performance against performance usingthe different matching algorithm.

In embodiments, a method may comprise: obtaining for each call in oneset of calls, by the one or more computers, a respective patternrepresenting one or multiple different respective data fields;obtaining, by the one or more computers, performance data for therespective patterns of the calls; obtaining, by the one or morecomputers, agent performance data for respective agents in a set ofagents; determining, by the one or more computers, agent performancesensitivity to call pattern performance comprising the agent performancedata correlated to the pattern performance data for the calls the agenthas handled; grouping, by the one or more computers, the set of agentsinto at least two groups comprising one group and a different groupbased at least in part on the agent performance data; matching for theone group of the agents, by the one or more computers, a respective oneof the agents from the one group to one of the calls based at least inpart on the performance data for the pattern of the one call and theperformance data of the respective one agent in the one group; andmatching for the different group of the agents, by the one or morecomputers, a respective one of the agents from the different group ofagents to a different one of the calls based at least in part on theperformance data for the pattern of the one call and the agentperformance sensitivity to call pattern performance for the one agent inthe different group. Note that in other embodiments, the groupings maybe by ranges of other parameters, such as call count ranges, or callhandle time ranges, or regions, or demographic data, to name a few. Thebreakpoint between the ranges may be determined empirically and/or basedon the availability of data for that element.

In embodiments, the determining agent performance sensitivity to callpattern performance for agents in a set of agents may comprisecorrelating agent performance to call performance data for the calls theagent has handled in a data set.

In embodiments, the method may further comprise: percentiling orranking, by the one or more computers, the respective patterns in theset of calls based at least in part on their respective performancedata; and percentiling or ranking, by the one or more computers, theagents in the set of agents based at least in part on the agentperformance data; percentiling or ranking, by the one or more computers,the agents in the set of agents based at least in part on their agentperformance sensitivity to call pattern performance; wherein thematching for the one group is based at least in part on the percentileor ranking of the respective pattern of the one call and the percentileor ranking of the one agent in the one group, and wherein the matchingfor the different group is based at least in part on the percentile orranking of the respective pattern of the one call and the percentile orranking by agent sensitivity to call performance of the one agent in thedifferent group.

In embodiments, the method may further comprise: percentiling orranking, by the one or more computers, the agents based at least in parton the agent performance data; wherein the grouping may be based atleast in part on the performance percentiles or rankings of the agents,with the agents in one of the groups having lower percentiles orrankings than agents in the different group. In embodiments, apercentile or ranking break point between the one group and thedifferent group may be determined based on one or more criteria.

In embodiments, the matching steps may be performed concurrently. Inembodiments, the matching steps may be performed consecutively or withpartial overlap in time.

In embodiments, the method may further comprise: matching, by the one ormore computers, a different set of calls to agents using a differentmatching algorithm; comparing, performance from call-agent matches ofthe different set of calls using the different matching algorithm withperformance for the one set of calls using a combination of the matchingof the one group of agents to calls and the matching of the differentgroup of agents to calls; and generating, by the one or more computers,a report or display data for the performance comparing results fromusing the combination of matching algorithms against performance usingthe different matching algorithm.

In embodiments, a system may comprise: one or more computers configuredwith the following components: a call data extractor component forobtaining for each call in one set of calls a respective patternrepresenting one or multiple different respective data fields; a callpattern performance extractor for obtaining performance data for therespective patterns of the calls; an agent performance extractorcomponent for obtaining performance data for respective agents in a setof agents; an agent sensitivity to call performance correlation enginefor determining agent performance sensitivity to call patternperformance for agents in a set of agents comprising the agentperformance data correlated to the call performance data for the callsthe agent has handled; and a matching engine for matching a respectiveone of the agents from the set of agents to one of the calls based atleast in part on the performance data for the respective pattern of theone call and on the agent sensitivity to call performance for therespective one agent of the set of agents.

In embodiments, the system may further comprise: a first percentile orranking engine configured for percentiling or ranking the patterns inthe set of calls based at least in part on their respective performancedata; and a second percentile or ranking engine configured forpercentiling or ranking the respective agents in the set of agents basedat least in part on their respective agent performance sensitivity tocall performance; wherein the matching engine is configured to performmatching based at least in part on the percentile or ranking of the onecall and the percentile or ranking by agent performance sensitivity tocall performance.

In embodiments, a system may comprise: one or more computers configuredwith the following components: a call data extractor component forobtaining for each call in one set of calls a respective patternrepresenting one multiple different respective data fields; a callpattern performance extractor for obtaining performance data for therespective patterns of the calls; an agent performance extractorcomponent for obtaining agent performance data for respective agents ina set of agents; an agent performance sensitivity to call patternperformance correlation engine for determining agent sensitivity to callperformance comprising the agent performance data correlated to thepattern performance data for the calls the agent has handled; a groupingengine configured for grouping the set of agents into at least twogroups comprising one group and a different group based at least in parton the agent performance data; a first matching engine configured formatching a respective one of the agents from the one group to one of thecalls based at least in part on the performance data for the pattern ofthe one call and the performance data of the respective one agent in theone group; and a second matching engine configured for matching for thedifferent group of the agents a respective one of the agents from thedifferent group of agents to a different one of the calls based at leastin part on the performance data for the pattern of the one call and theagent sensitivity to call performance for the one agent in the differentgroup. Note that in other embodiments, the groupings may be by ranges ofother parameters, such as call handle time ranges, or regions, ordemographic data, to name a few. The breakpoint between the ranges maybe determined empirically and/or by data availability for the parameteror by one or more other parameters.

In embodiments, the system may further comprise: a first percentile orranking engine for percentiling or ranking the respective patterns inthe set of calls based at least in part on their respective performancedata; a second percentile or ranking engine for percentiling or rankingthe agents in the set of agents based at least in part on the agentperformance data; a third percentile or ranking engine for percentilingor ranking the agents in the set of agents based at least in part ontheir agent performance sensitivity to call pattern performance; whereinthe first matching engine is configured for matching the one group basedat least in part on the percentile or ranking of the respective patternof the one call and the percentile or ranking of the one agent in theone group, and wherein the second matching engine is configured formatching the different group based at least in part on the percentile orranking of the respective pattern of the one call and the percentile orranking by agent sensitivity to call performance of the one agent in thedifferent group.

In embodiments, the system may further comprise: a percentile or rankingengine for percentiling or ranking the agents based at least in part onthe agent performance data; wherein the grouping may be based at leastin part on the performance percentiles or rankings of the agents, withthe agents in one of the groups having lower percentiles or rankingsthan agents in the different group. In embodiments, the grouping enginemay be configured for setting a percentile or ranking break pointbetween the one group and the different group based on one or morecriteria.

In embodiments, a method may comprise: obtaining for each call in oneset of calls, by the one or more computers, a respective patternrepresenting one multiple different respective data fields; obtaining,by the one or more computers, performance data for the respectivepatterns of the calls; obtaining, by the one or more computers,performance data for respective agents in a set of agents; determining,by the one or more computers, agent performance sensitivity to callpattern performance for agents in a set of agents comprising the agentperformance data correlated to the call performance data for the callsthe agent has handled; determining, by the one or more computers,pattern performance sensitivity to agent performance comprising thepattern performance data correlated to the agent performance data; andmatching, by the one or more computers, a respective one of the agentsfrom the set of agents to one of the calls based at least in part on thepattern performance sensitivity to agent performance for the respectivepattern of the one call and on the agent sensitivity to call performancefor the respective one agent of the set of agents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram reflecting the general setup of a contact centeroperation.

FIG. 2 illustrates an exemplary routing system having a routing enginefor routing callers based on performance and/or pattern matchingalgorithms.

FIG. 3 illustrates an exemplary routing system having a mapping enginefor routing callers based on performance and/or pattern matchingalgorithms.

FIG. 4 illustrates an exemplary method for matching a first portion ofcallers and agents using caller data and agent data in a patternmatching algorithm and a second portion of callers using queue order.

FIG. 5 illustrates an exemplary interface having a graphic element foradjusting the number or fraction of callers for routing based onperformance and/or pattern matching algorithms.

FIG. 6 illustrates a typical computing system that may be employed toimplement some or all processing functionality in certain embodiments ofthe present disclosure.

FIG. 7 is a flowchart reflecting embodiments of the present disclosureperforming matching using pattern performance sensitivity to agentperformance.

FIG. 8A is a flowchart reflecting further embodiments of the presentdisclosure for performing matching using pattern performance sensitivityto agent performance.

FIG. 8B is a continuation of the Flowchart of FIG. 8A.

FIG. 9 is a flowchart reflecting embodiments of the present disclosurefor performing matching using agent sensitivity to call performance.

FIG. 10A is a flowchart reflecting further embodiments of the presentdisclosure for performing matching using agent sensitivity to callperformance.

FIG. 10B is a continuation of the Flowchart of FIG. 10A.

FIG. 11 is a system embodiment for implementing embodiments describedherein based at least in part on call pattern sensitivity to agentperformance.

FIG. 12 is a system embodiment for implementing embodiments describedherein based at least in part on groupings using multiple matchingalgorithms and call pattern sensitivity to agent performance.

FIG. 13 is a system embodiment for implementing embodiments describedherein based at least in part on agent sensitivity to call performance.

FIG. 14 is a system embodiment for implementing embodiments describedherein based at least in part on groupings using multiple matchingalgorithms and call pattern sensitivity to agent performance.

DETAILED DESCRIPTION OF EMBODIMENTS

The following description is presented to enable a person of ordinaryskill in the art to make and use embodiments of the present disclosure,and is provided in the context of particular applications and theirrequirements. Various modifications to the embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments and applications withoutdeparting from the spirit and scope of the present disclosure. Moreover,in the following description, numerous details are set forth for thepurpose of explanation. However, one of ordinary skill in the art willrealize that embodiments of the present disclosure might be practicedwithout the use of these specific details. In other instances,well-known structures and devices are shown in block diagram form inorder not to obscure the description of the present disclosure withunnecessary detail. Thus, the present disclosure is not intended to belimited to the embodiments shown, but is to be accorded the widest scopeconsistent with the principles and features disclosed herein.

While the present disclosure is described in terms of particularexamples and illustrative figures, those of ordinary skill in the artwill recognize that the present disclosure is not limited to theexamples or figures described. Those skilled in the art will recognizethat the operations of the various embodiments may be implemented usinghardware, software, firmware, or combinations thereof, as appropriate.For example, some processes can be carried out using processors or otherdigital circuitry under the control of software, firmware, or hard-wiredlogic. (The term “logic” herein refers to fixed hardware, programmablelogic and/or an appropriate combination thereof, as would be recognizedby one skilled in the art to carry out the recited functions.) Softwareand firmware can be stored on computer-readable storage media. Someother processes can be implemented using analog circuitry, as is wellknown to one of ordinary skill in the art. Additionally, memory or otherstorage, as well as communication components, may be employed inembodiments in accordance with the present disclosure.

Exemplary call mapping and routing systems and methods are described,for example, in “Systems and Methods for Routing Callers to an Agent ina Contact Center,” filed on Jul. 25, 2008, bearing Ser. No. 12/180,382(now U.S. Pat. No. 8,359,219); in U.S. patent application Ser. No.12/267,471, entitled “Routing Callers to Agents Based on Time EffectData,” filed on Nov. 7, 2008; Ser. No. 12/490,949, entitled “ProbabilityMultiplier Process for Call Center Routing,” filed on Jun. 24, 2009; andSer. No. 12/266,418, entitled, “Pooling Callers for Matching to AgentsBased on Pattern Matching Algorithms,” filed on Nov. 6, 2008, U.S.patent application Ser. No. 12/051,251 filed on Jan. 28, 2008; U.S.patent application Ser. No. 12/267,471 filed on Jan. 28, 2010;provisional application No. 61/084,201 filed Jul. 28, 2008, U.S. patentapplication Ser. No. 13/843,807 filed on Mar. 15, 2013; U.S. patentapplication Ser. No. 13/843,541 filed on Mar. 13, 2013; U.S. patentapplication Ser. No. 13/843,724 filed on Mar. 15, 2013, and applicationSer. No. 14/032,657 filed Sep. 20, 2013, titled “Use of Abstracted Datain Pattern Matching System,” all of which are incorporated herein byreference in their entirety.

FIG. 1 is a diagram reflecting the general setup of a contact centeroperation 100. The network cloud 101 reflects a specific or regionaltelecommunications network designed to receive incoming callers or tosupport contacts made to outgoing callers. The network cloud 101 cancomprise a single contact address, such as a telephone number or emailaddress, or multiple contract addresses. The central router 102 reflectscontact routing hardware and software designed to help route contactsamong call centers 103. The central router 102 may not be needed wherethere is only a single contact center deployed. Where multiple contactcenters are deployed, more routers may be needed to route contacts toanother router for a specific contact center 103. At the contact centerlevel 103, a contact center router 104 will route a contact to an agent105 with an individual telephone or other telecommunications equipment105. Typically, there are multiple agents 105 at a contact center 103,though there are certainly embodiments where only one agent 105 is atthe contact center 103, in which case a contact center router 104 mayprove to be unnecessary.

FIG. 2 illustrates an exemplary contact center routing system 200 (whichmay be included with contact center router 104 of FIG. 1). Broadlyspeaking, routing system 200 is operable to match callers and agentsbased, at least in part, on agent performance or pattern matchingalgorithms using caller data and/or agent data. Routing system 200 mayinclude a communication server 202 and a routing engine 204 (referred toat times as “SatMap” or “Satisfaction Mapping”) for receiving andmatching callers to agents (referred to at times as “mapping” callers toagents).

Routing engine 204 may operate in various manners to match callers toagents based on performance data of agents, pattern matching algorithms,and computer models, which may adapt over time based on the performanceor outcomes of previous caller-agent matches. In one example, therouting engine 204 includes a neural network based adaptive patternmatching engine. Various other exemplary pattern matching and computermodel systems and methods which may be included with content routingsystem and/or routing engine 204 are described, for example, in U.S.patent application Ser. No. 12/021,251, filed Jan. 28, 2008, and U.S.Serial No. U.S. patent application Ser. No. 12/202,091, filed Aug. 29,2008, both of which are hereby incorporated by reference in theirentirety. Of course, it will be recognized that other performance basedor pattern matching algorithms and methods may be used alone or incombination with those described here.

Routing system 200 may further include other components such ascollector 206 for collecting caller data of incoming callers, dataregarding caller-agent pairs, outcomes of caller-agent pairs, agent dataof agents, and the like. In embodiments to be described below, thecollector may be configured in the one or more computers to obtainperformance data for agents in a set of agents. In embodiments, thecollector may further be configured to obtain a respective abstracteddata stream for each of multiple of the calls, with each respective datastream having multiple different locations along the abstracted datastream representing multiple different respective fields, wherein themeaning for the field data in the respective different locations for thedifferent respective fields is not known by the system, and to determinefrom respective field data positions in the respective data streamsrespective patterns for the respective data streams.

In embodiments, the collector may further be configured in the one ormore computers to obtain performance data for the respective patterns.In yet further embodiments, the collector may be configured to rearrangethe data to an order of a data set to be used to correlation where theabstracted data stream is scrambled. In embodiments, the collector mayfurther be configured in the one or more computers to determine fieldpositions for the fields in the abstracted data stream; and to selectonly the determined field positions from the data stream to form thepattern. In embodiments, for multiple abstracted data streams receivedduring the period, the abstracted data for the different fields may bereceived in two or more different orders over the course of the periodof time, and the collector may be further configured in the one or morecomputers to arrange the field data for the different fields for thedata streams of the respective calls in a same order.

Further, routing system 200 may include a reporting engine 208 forgenerating reports of performance and operation of routing system 200.Various other servers, components, and functionality are possible forinclusion with routing system 200. Further, although shown as a singlehardware device, it will be appreciated that various components may belocated remotely from each other (e.g., communication server 202 androuting engine 204 need not be included with a common hardware/serversystem or included at a common location). Additionally, various othercomponents and functionality may be included with routing system 200,but have been omitted here for clarity.

FIG. 3 illustrates detail of exemplary routing engine 204. Routingengine 204 includes a main mapping engine 304, which receives callerdata and agent data from databases 310 and 312. In some examples,routing engine 204 may route callers based solely or in part onperformance data associated with agents. In other examples, routingengine 204 may make routing decisions based solely or in part oncomparing various caller data and agent data, which may include, e.g.,performance based data, demographic data, psychographic data, and otherbusiness-relevant data. Additionally, affinity databases (not shown) maybe used and such information received by routing engine 204 for makingrouting decisions.

In one example, routing engine 204 includes or is in communication withone or more neural network engines 306. Neural network engines 306 mayreceive caller and agent data directly or via routing engine 204 andoperate to match and route callers based on pattern matching algorithmsand computer models generated to increase the chances of desiredoutcomes. Further, as indicated in FIG. 3, call history data (including,e.g., caller-agent pair outcomes with respect to cost, revenue, customersatisfaction, etc.) may be used to retrain or modify the neural networkengine 306.

Routing engine 204 further includes or is in communication with holdqueue 308, which may store or access hold or idle times of callers andagents, and operate to map callers to agents based on queue order of thecallers (and/or agents). Mapping engine 304 may operate, for example, tomap callers based on a pattern matching algorithm, e.g., as includedwith neural network engine 306, or based on queue order, e.g., asretrieved from hold queue 308. In particular, mapping engine 304 mappinga first portion of callers based on one or more of performance baseddata, pattern matching algorithm(s), or computer model(s). Additionally,mapping engine 304 may route a second portion of callers based on aqueue order of the callers, preferentially routing those callers thathave been held the longest (a similar queue and preferentially routingmay be used if multiple agents are available for a caller). It should benoted that other essentially random routing methods may be used in placeof queue routing, and which generally serve as a control for comparingwith the performance and/or pattern matching mapping methods described.

FIG. 4 illustrates an exemplary method for mapping and routing callersto agents where a first portion or fraction of callers is routed basedon a performance based and/or pattern matching algorithm and a secondportion or fraction of callers is routed based on conventional,essentially random, routing method such as queue based routing.Accordingly, a routing system first determines how the caller is to berouted at 420. For instance, the system may map callers and agents invarious ratios depending on the settings input by the contact center.For example, if the setting is at 80, or 80%, the system would map 80%of the caller-agent pairs based on performance and/or pattern matchingalgorithms and the remaining 20% of caller-agent pairs based on othermethods such as queue order.

Exemplary performance based and/or pattern matching methods for routingcallers to agents includes rating agents on performance, comparing agentdata and caller data and matching per a pattern matching algorithm,creating computer models to predict outcomes of agent-caller pairs, orcombinations thereof. In particular, one exemplary method for increasingthe chances of an optimal interaction includes combining agent grades(which may be determined from grading or ranking agents on desiredoutcomes), agent demographic data, agent psychographic data, and otherbusiness-relevant data about the agent (individually or collectivelyreferred to in this application as “agent data”), along withdemographic, psychographic, and other business-relevant data aboutcallers (individually or collectively referred to in this application as“caller data”). Agent and caller demographic data can comprise any of:gender, age, education, accent, income, wealth, nationality, ethnicity,area code, zip code, marital status, job status, credit score, and thelike. Agent and caller psychographic data can comprise any ofintroversion, sociability, desire for financial success, film andtelevision preferences, and the like.

The exemplary method may include determining caller data associated withone or more callers (e.g., a caller on hold), determining agent dataassociated with one or more agents (e.g., one or more available agents),comparing the agent data and the caller data (e.g., via a patternmatching algorithm), and matching the caller to an agent to increase thechance of an optimal interaction. In particular, at 422, caller data(such as a caller demographic or psychographic data) is determined oridentified for a caller. One way of accomplishing this is by retrievingcaller data from available databases by using the caller's contactinformation as an index. Available databases include, but are notlimited to, those that are publicly available, those that arecommercially available, or those created by a contact center or acontact center client. In an outbound contact center environment, thecaller's contact information is generally known beforehand. In aninbound contact center environment, the caller's contact information canbe retrieved by examining the caller's CallerID information or byrequesting this information of the caller at the outset of the contact,such as through entry of a caller account number or othercaller-identifying information. Other business-relevant data such ashistoric purchase behavior, current level of satisfaction as a customer,or volunteered level of interest in a product may also be retrieved fromavailable databases.

At 424, agent data for one or more agents is identified or determined.One method of determining agent demographic or psychographic data caninvolve surveying agents at the time of their employment or periodicallythroughout their employment. Such a survey process can be manual, suchas through a paper or oral survey, or automated with the survey beingconducted over a computer system, such as by deployment over aweb-browser. In some example, the method uses agent grades, demographic,psychographic, and other business-relevant data, along with callerdemographic, psychographic, and other business-relevant data, otherembodiments of the exemplary methods and systems can eliminate one ormore types or categories of caller or agent data to reduce the time toanswer, computing power, or storage necessary.

The agent data and caller data may then be compared at 426. Forinstance, the agent data and caller data can be passed to acomputational system for comparing caller data to agent data for eachagent-caller pair, e.g., the caller data and agent data is compared in apair-wise fashion for each potential routing decision. In one example,the comparison is achieved by passing the agent and caller data to apattern matching algorithm to create a computer model that matches eachcaller with each agent and estimates the probable outcome of eachmatching along a number of optimal interactions, such as the generationof a sale, the duration of contact, or the likelihood of generating aninteraction that a customer finds satisfying.

The pattern matching algorithm to be used in the exemplary methods andsystem can comprise any correlation algorithm, such as a neural networkalgorithm or a genetic algorithm. To generally train or otherwise refinethe algorithm, actual contact results (as measured for an optimalinteraction) are compared against the actual agent and caller data foreach contact that occurred. The pattern matching algorithm can thenlearn, or improve its learning of, how matching certain callers withcertain agents will change the chance of an optimal interaction. In thismanner, the pattern matching algorithm can then be used to predict thechance of an optimal interaction in the context of matching a callerwith a particular set of caller data, with an agent of a particular setof agent data. Preferably, the pattern matching algorithm isperiodically refined as more actual data on caller interactions becomesavailable to it, such as periodically training the algorithm every nightafter a contact center has finished operating for the day.

The pattern matching algorithm may create or use a computer modelreflecting the predicted chances of an optimal interaction for eachagent and caller matching. Preferably, the computer model will comprisethe predicted chances for a set of optimal interactions for every agentthat is logged in to the contact center as matched against everyavailable caller. Alternatively, the computer model can comprise subsetsof these, or sets containing the aforementioned sets. For example,instead of matching every agent logged into the contact center withevery available caller, examples can match every available agent withevery available caller, or even a narrower subset of agents or callers.Likewise, embodiments of the present disclosure can match every agentthat ever worked on a particular campaign—whether available or logged inor not—with every available caller. Similarly, the computer model cancomprise predicted chances for one optimal interaction or a number ofoptimal interactions.

A computer model can also comprise a suitability score for each matchingof an agent and a caller. The suitability score can be determined bytaking the chances of a set of optimal interactions as predicted by thepattern matching algorithm, and weighting those chances to place more orless emphasis on a particular optimal interaction as related to anotheroptimal interaction. The suitability score can then be used in theexemplary methods and systems to determine which agents should beconnected to which callers.

Based on the pattern matching algorithm and/or computer model, themethod further includes determining the agent having the best match tothe caller at 428. As will be understood, the best matching agent maydepend on the pattern matching algorithm, computer model, and desiredoutput variables and weightings selected by a particular call center.The caller is then routed to the best matching agent at 430.

If the caller is selected at 420 for mapping to an agent by a differentmethod (e.g., not based on a performance and/or pattern matchingalgorithm), this particular exemplary method includes routing via anAutomatic Call Distribution (ACD) queue order or the like by determininga queue order of the caller, if applicable, at 450. For example, ifother callers are on hold waiting for an available agent, the caller maybe queued with other callers, e.g., a system may order the callers interms of hold time and preferentially map those callers that have beenholding the longest. Similarly, the exemplary method includesdetermining a queue order of the agents, if applicable, at 452 (forexample, in a situation where multiple agents are available).Accordingly, the system generally operates to map the agent that hasbeen waiting or idle the longest with the caller that has been holdingthe longest. The caller may then be routed to the agent at 454.

It is noted that in other examples, where callers are matched with atleast a pattern matching algorithm (e.g., alone or in combination withperformance based ranking of the agents), the different method mayinclude performance based routing. This allows for comparing orbenchmarking the pattern matching algorithm against performance basedrouting.

According to another aspect of the exemplary systems and methodsdescribed, a visual computer interface and printable reports may beprovided to the contact center or their clients to allow them to, in areal-time or a past performance basis, monitor the statistics of agentto caller matches, measure the optimal interactions that are beingachieved versus the interactions predicted by the computer model, aswell as any other measurements of real time or past performance usingthe methods described herein. A visual computer interface for changingthe number or portion of callers that are mapped via performance and/orpattern matching algorithms (as well as the weighting on an optimalinteraction) can also be provided to the contact center or the contactcenter client, such that they can, as discussed herein, monitor theeffect of the performance based data and/or pattern matching algorithmson one or more outcome variables.

FIG. 5 illustrates an exemplary interface 500 having a graphic element502 for adjusting the fraction or portion of callers that are mappedaccording to performance and/or pattern matching algorithms. It will berecognized that interface 500 may be displayed within a browser page,portal page, or standalone user interface for a contact center routingsystem. Additionally, various other information and functionality may beincluded with interface 500, but is omitted here for clarity.

In this example, interface 500 displays a report of call centerperformance broken down by different output variables at 510, 512, and514. In particular, cost, revenue generation, and customer satisfactionare illustrated, but other output variables such as first callresolution, cancellation, or other variable outputs from the patternmatching algorithm(s) or computer model(s) of the system may bedisplayed. Interface 500 further includes settings for desiredweightings of different outcome variables of the pattern matchingalgorithms and computer models being used for routing callers to agentsat 504. In particular, selector 504 includes selectors for adjusting theweighting of revenue, cost, and customer satisfaction in the call centerrouting algorithms and computer models. Various weighting methods andalgorithms are described, for example, in co-pending U.S. patentapplication Ser. No. 12/202,091, filed Aug. 29, 2008, which isincorporated herein by reference in its entirety. Of course, variousother pattern matching algorithms, computer models, and weightingmethods for adjusting the desired outcomes are possible andcontemplated.

Selector 502 operates to adjust the “power” of the mapping system, e.g.,the portion or percentage of callers that are mapped via performanceand/or pattern matching algorithms as described. In this example, ifselector 502 is set to “100” the system routes all callers via theperformance and/or pattern matching algorithms; alternatively, ifselector 502 is set to “0” the system does not route any callers via theperformance and/or pattern matching algorithms. Selector 502 may beadjusted in response to input from a mouse, input to a key board (e.g.,arrow keys, numerical entries, and so on), or the like. Further,selector 502 may be replaced or further include a “slider” element,drop-down selector, entry field for manually entering numbers or values,up-and-down arrows, and so on.

As described, routing a fraction of callers by an essentially randomprocess provides an evaluation of the performance and/or patternmatching algorithms of the mapping system. For example, outcomevariables can be compared for callers routed via the mapping system andthose routed otherwise. For instance, interface 500 includes a display510 of cost over time for the routing system with the mapping system onand off (i.e., “SatMap On” and “SatMap Off”) as indicated by 511 a and511 b respectively. Display 510 illustrates that the cost is lower forcallers routed via the mapping system than those mapped differently(e.g., by queue order or essentially randomly). As indicated in display512, revenue for callers routed via the mapping system, shown by 513 a,is greater than for other callers, shown by 513 b. Further, as indicatedin display 514, customer satisfaction for callers routed via the mappingsystem, shown by 515 a, is greater than for other callers, shown by 515b. Note that in embodiments, the matching via the pattern matchingalgorithm may result in frequently reducing the length of time of thecalls.

It is noted that the information displayed by displays 510, 512, and 514are of past performance data; however, in other examples, interface 500may further operate to display estimated effects on one or more outcomevariables by changing selector 502. For instance, displaying theprobable change in one or more of cost, revenue generation, or customersatisfaction by changing selector 502. Various estimation methods andalgorithms for estimating outcome variables are described, for example,in co-pending U.S. provisional Patent application Ser. No. 61/084,201,filed on Jul. 28, 2008, and which is incorporated herein by reference inits entirety. In one example, the estimate includes evaluating a pasttime period of the same (or similar) set of agents and constructing adistribution of agent/caller pairs. Using each pair, an expected successrate can be computed via the pattern matching algorithm and applied tocurrent information to estimate current performance (e.g., with respectto one or more of sales, cost, customer satisfaction, etc.).Accordingly, taking historical call data and agent information thealgorithm can compute estimates of changing the power or number ofcallers mapped via the performance and/or pattern matching algorithms.It is noted that a comparable time (e.g., time of day, day of the weeketc.) for the historical information may be important as performancewill likely vary with time.

As noted, systems and methods of the present disclosure can be used tooptimize the routing of callers to agents in a contact center. Examplesof typical optimal interactions include increasing sales, decreasing theduration of the contact (and hence the cost to the contact center),providing for an acceptable level of customer satisfaction, or any otherinteraction that a contact center may seek to control or optimize.) Thesystems and methods of the present disclosure can improve the chance ofan optimal interaction by, in general, grading agents on an optimalinteraction, and matching a graded agent with a caller to increase thechance of the optimal interaction. In a more advanced embodiment, thesystems and methods of the present disclosure can also be used toincrease the chance of an optimal interaction by matching a caller to anagent using a computer model derived from data describing demographic,psychographic, past purchase behavior, or other business-relevantinformation about a caller, together with data describing demographic,psychographic, or historical performance about an agent.

As noted, in a relatively basic embodiment of the present disclosure,the performance of a contact center's agents may be collated over aperiod time to grade each agent on their ability to achieve an optimalinteraction. The period of time can be as short as the immediately priorcontact to a period extending as long as the agent's first interactionwith a caller. The grade determined for each agent is then used as afactor in matching and connecting a caller to a particular agent. Forexample, certain agents may be shown to have a greater ability togenerate sales than that of other agents engaged in the same contactcenter. The embodiments of present disclosure, by preferentially routingcallers to those agents shown to have greater ability to generate sales,can increase the chances of achieving greater sales during the contacts.Similarly, other agents may be shown to generate shorter interactionswith callers than that of other agents at the same contact center. Bypreferentially routing contacts to the agents shown to generate shorterinteractions with callers, a contact center or contact center client candecrease its overall need for agents and communication bandwidth, andtherefore, reduce its costs.

In general, by grading the agents at a contact center on their abilityto achieve an optimal interaction, the contact center can match andconnect callers to agents to increase the chance of achieving anyoptimal interaction that may be chosen. The method of grading agent canbe as simple as ranking each agent on a scale of 1 to N for a particularoptimal interaction, with N being the total number of agents. The methodof grading can also comprise determining the average contact handle timeof each agent to grade the agents on cost, determining the total salesrevenue or number of sales generated by each agent to grade the agentson sales, or conducting customer surveys at the end of contacts withcallers to grade the agents on customer satisfaction. The foregoing,however, are only examples of how agents may be graded; many othermethods exist.

If agents are graded on more than one optimal interaction, embodimentsof the present disclosure can be configured to weight optimalinteractions to ascertain which callers should be routed to which agent.For example, if there were two currently available agents for anindividual caller, and an embodiment of the present disclosure estimatedthat routing the caller to one agent would result in a higher likelihoodof a sale occurring, while routing the caller to the other agent wouldresult in a shorter duration contact, depending on which optimalinteraction the embodiment of the present disclosure was weighting moreheavily, the caller may be routed to either the first or the secondagent. In another example, if an embodiment of the present disclosureestimated that routing the caller to one agent would result in a highlikelihood of a sale, a short contact duration, but a low level ofcustomer satisfaction, while routing the caller to another agent wouldresult in a high likelihood of a sale, a longer contact duration, but ahigher level of customer satisfaction, depending on which mix of optimalinteractions an embodiment of the present disclosure was weighting moreheavily, the caller may be routed to the first or second agent.

The weightings placed on the various optimal interactions can take placein real-time in a manner controlled by the contact center, its clients,or in line with predetermined rules. Optionally, the contact center orits clients may control the weighting over the internet or some anotherdata transfer system. As an example, a client of the contact centercould access the weightings currently in use over an internet browserand modify these remotely. Such a modification may be set to takeimmediate effect and, immediately after such a modification, subsequentcaller routings occur in line with the newly establishing weightings. Aninstance of such an example may arise in a case where a contact centerclient decides that the most important strategic priority in theirbusiness at present is the maximization of revenues. In such a case, theclient would remotely set the weightings to favor the selection ofagents that would generate the greatest probability of a sale in a givencontact. Subsequently the client may take the view that maximization ofcustomer satisfaction is more important for their business. In thisevent, they can remotely set the weightings of embodiments of thepresent disclosure such that callers are routed to agents most likely tomaximize their level of satisfaction. Alternatively the change inweighting may be set to take effect at a subsequent time, for instance,commencing the following morning.

In an outbound contact center environment employing telephone devices,the matching that takes place can be reflected in the form of a leadlist. The lead list can be for one particular agent or a group ofagents, who can then call through the lead list to conduct theirsolicitation efforts. Where a dialer is used to call through a leadlist, upon obtaining a live caller, embodiments of the presentdisclosure can determine the available agents, match the live callerwith one or more of the available agents, and connect the caller withone of those agents. Preferably, embodiments of the present disclosurewill match the live caller with a group of agents, define an ordering ofagent suitability for the caller, match the live caller to thehighest-graded graded agent currently available in that ordering, andconnect the caller to the highest-graded agent. In this manner, use of adialer becomes more efficient in embodiments of the present disclosure,as the dialer should be able to continuously call through a lead listand obtain live callers as quickly as possible, which embodiments of thepresent disclosure can then match and connect to the highest gradedagent currently available.

In a more advanced embodiment, the system and methods of the presentdisclosure can be used to increase the chances of an optimal interactionby combining agent grades, agent demographic data, agent psychographicdata, and other business-relevant data about the agent (individually orcollectively referred to in this application as “agent data”), alongwith demographic, psychographic, and other business-relevant data aboutcallers (individually or collectively referred to in this application as“caller data”). Agent and caller demographic data can comprise any of:gender, age, education, accent, income, wealth, nationality, ethnicity,area code, zip code, marital status, job status, and credit score. Agentand caller psychographic data can comprise any of introversion,sociability, desire for financial success, and film and televisionpreferences.

Once agent data and caller data have been collected, this data is passedto a computational system. The computational system then, in turn, usesthis data in a pattern matching algorithm to create a computer modelthat matches each agent with each caller and estimates the probableoutcome of each matching along a number of optimal interactions, such asthe generation of a sale, the duration of contact, or the likelihood ofgenerating an interaction that a customer finds satisfying. As anexample, embodiments of the present disclosure may indicate that, bymatching a caller to a female agent, the matching will increase theprobability of a sale by 4 percent, reduce the duration of a contact by6 percent, and increase the satisfaction of the caller with theinteraction by 12 percent. Generally, embodiments of the presentdisclosure will generate more complex predictions spanning multipledemographic and psychographic aspects of agents and callers. Theembodiments of present disclosure might conclude, for instance, that acaller if connected to a single, white, male, 25 year old, agent thathas high speed internet in his home and enjoys comedic films will resultin a 12 percent increase in the probability of a sale, a 7 percentincrease in the duration of the contact, and a 2 percent decrease in thecaller's satisfaction with the contact. In parallel, embodiments of thepresent disclosure may also determine that the caller if connected to amarried, black, female, 55 year old agent will result in a 4 percentincrease in the probability of a sale, a 6 percent decrease in theduration of a contact, and a 9 percent increase in the caller'ssatisfaction with the contact.

It may be that the computer model indicates that a caller match withagent one will result in a high chance of a sale with but a high chanceof a long contact, while a caller match with agent two will result in alow chance of a sale but a high chance of a short contact. If an optimalinteraction for a sale is more heavily weighted than an optimalinteraction of low cost, then the suitability scores for agent one ascompared to agent two will indicate that the caller should be connectedto agent one. If, on the other hand, an optimal interaction for a saleis less weighted than an optimal interaction for a low cost contact, thesuitability score for agent two as compared to agent one will indicatethat the caller should be connected to agent two.

One aspect of the present disclosure is that embodiments may developaffinity databases by storing data, the databases comprising data on anindividual caller's contact outcomes (referred to in this application as“caller affinity data”), independent of their demographic,psychographic, or other business-relevant information. Such calleraffinity data can include the caller's purchase history, contact timehistory, or customer satisfaction history. These histories can begeneral, such as the caller's general history for purchasing products,average contact time with an agent, or average customer satisfactionratings. These histories can also be agent specific, such as thecaller's purchase, contact time, or customer satisfaction history whenconnected to a particular agent.

The caller affinity data can then be used to refine the matches that canbe made using embodiments of the present disclosure. As an example, acertain caller may be identified by their caller affinity data as onehighly likely to make a purchase, because in the last several instancesin which the caller was contacted, the caller elected to purchase aproduct or service. This purchase history can then be used toappropriately refine matches such that the caller is preferentiallymatched with an agent deemed suitable for the caller to increase thechances of an optimal interaction. Using this embodiment, a contactcenter could preferentially match the caller with an agent who does nothave a high grade for generating revenue or who would not otherwise bean acceptable match, because the chance of a sale is still likely giventhe caller's past purchase behavior. This strategy for matching wouldleave available other agents who could have otherwise been occupied witha contact interaction with the caller. Alternatively, the contact centermay instead seek to guarantee that the caller is matched with an agentwith a high grade for generating revenue, irrespective of what thematches generated using caller data and agent demographic orpsychographic data may indicate.

A more advanced affinity database developed in accordance with thepresent disclosure is one in which a caller's contact outcomes aretracked across the various agent data. Such an analysis might indicate,for example, that the caller is most likely to be satisfied with acontact if they are matched to an agent of similar gender, age, or othercharacteristic of a specific agent. Using this approach, embodiments ofthe present disclosure could preferentially match a caller with aspecific agent or type of agent that is known from the caller affinitydata to have generated an acceptable optimal interaction.

Affinity databases can provide particularly actionable information abouta caller when commercial, client, or publicly-available database sourcesmay lack information about the caller. This database development canalso be used to further enhance contact routing and agent-to-callermatching even in the event that there is available data on the caller,as it may drive the conclusion that the individual caller's contactoutcomes may vary from what the commercial databases might imply. As anexample, if an embodiment of the present disclosure was to rely solelyon commercial databases in order to match a caller and agent, it maypredict that the caller would be best matched to an agent of the samegender to achieve optimal customer satisfaction. However, by includingaffinity database information developed from prior interactions with thecaller, embodiments of the present disclosure might more accuratelypredict that the caller would be best matched to an agent of theopposite gender to achieve optimal customer satisfaction.

Another aspect of the present disclosure is that it may develop affinitydatabases that comprise revenue generation, cost, and customersatisfaction performance data of individual agents as matched withspecific caller demographic, psychographic, or other business-relevantcharacteristics (referred to in this application as “agent affinitydata”). An affinity database such as this may, for example, result inembodiments of the present disclosure predicting that a specific agentperforms best in interactions with callers of a similar age, and lesswell in interactions with a caller of a significantly older or youngerage. Similarly this type of affinity database may result in embodimentsof the present disclosure predicting that an agent with certain agentaffinity data handles callers originating from a particular geographymuch better than the agent handles callers from other geographies. Asanother example, embodiments of the present disclosure may predict thata particular agent performs well in circumstances in which that agent isconnected to an irate caller.

Though affinity databases are preferably used in combination with agentdata and caller data that pass through a pattern matching algorithm togenerate matches, information stored in affinity databases can also beused independently of agent data and caller data such that the affinityinformation is the only information used to generate matches.

The embodiments of present disclosure can also comprise connection rulesto define when or how to connect agents that are matched to a caller.The connection rules can be as simple as instructing the presentdisclosure to connect a caller according to the best match among allavailable agents with that particular caller. In this manner, callerhold time can be minimized. The connection rules can also be moreinvolved, such as instructing embodiments of the present disclosure toconnect a caller only when a minimum threshold match exists between anavailable agent and a caller, or to allow a defined period of time tosearch for a minimum matching or the best available matching at thattime. The connection rules can also purposefully keep certain agentsavailable while a search takes place for a potentially better match.

It is typical for a queue of callers on hold to form at a contactcenter. When a queue has formed it is desirable to minimize the holdtime of each caller in order to increase the chances of obtainingcustomer satisfaction and decreasing the cost of the contact, which costcan be, not only a function of the contact duration, but also a functionof the chance that a caller will drop the contact if the wait is toolong. After matching the caller with agents, the connection rules canthus be configured to comprise an algorithm for queue jumping, whereby afavorable match of a caller on hold and an available agent will resultin that caller “jumping” the queue by increasing the caller's connectionpriority so that the caller is passed to that agent first ahead ofothers in the chronologically listed queue. The queue jumping algorithmcan be further configured to automatically implement a trade-off betweenthe cost associated with keeping callers on hold against the benefit interms of the chance of an optimal interaction taking place if the calleris jumped up the queue, and jumping callers up the queue to increase theoverall chance of an optimal interaction taking place over time at anacceptable or minimum level of cost or chance of customer satisfaction.Callers can also be jumped up a queue if an affinity database indicatesthat an optimal interaction is particularly likely if the caller ismatched with a specific agent that is already available.

Ideally, the connection rules should be configured to avoid situationswhere matches between a caller in a queue and all logged-in agents arelikely to result in a small chance of a sale, but the cost of thecontact is long and the chances of customer satisfaction slim becausethe caller is kept on hold for a long time while an embodiment of thepresent disclosure waits for the most optimal agent to become available.By identifying such a caller and jumping the caller up the queue, thecontact center can avoid the situation where the overall chances of anoptimal interaction (e.g., a sale) are small, but the monetary andsatisfaction cost of the contact is high.

The embodiments of present disclosure may store data specific to eachrouted caller for subsequent analysis. For example, embodiments of thepresent disclosure can store data generated in any computer model,including the chances for an optimal interaction as predicted by thecomputer model, such as the chances of sales, contact durations,customer satisfaction, or other parameters. Such a store may includeactual data for the caller connection that was made, including the agentand caller data, whether a sale occurred, the duration of the contact,and the level of customer satisfaction. Such a store may also includeactual data for the agent to caller matches that were made, as well ashow, which, and when matches were considered pursuant to connectionrules and prior to connection to a particular agent.

This stored information may be analyzed in several ways. One possibleway is to analyze the cumulative effect of embodiments of the presentdisclosure on an optimal interaction over different intervals of timeand report that effect to the contact center or the contact centerclient. For example, embodiments of the present disclosure can reportback as to the cumulative impact of embodiments of the presentdisclosure in enhancing revenues, reducing costs, increasing customersatisfaction, over five minute, one hour, one month, one year, and othertime intervals, such as since the beginning of a particular clientsolicitation campaign. Similarly, embodiments of the present disclosurecan analyze the cumulative effect of embodiments of the presentdisclosure in enhancing revenue, reducing costs, and increasingsatisfaction over a specified number of callers, for instance 10callers, 100 callers, 1000 callers, the total number of callersprocessed, or other total numbers of callers.

One method for reporting the cumulative effect of employing embodimentsof the present disclosure comprises matching a caller with each agentlogged in at the contact center, averaging the chances of an optimalinteraction over each agent, determining which agent was connected tothe caller, dividing the chance of an optimal interaction for theconnected agent by the average chance, and generating a report of theresult. In this manner, the effect of embodiments of the presentdisclosure can be reported as the predicted increase associated withrouting a caller to a specific agent as opposed to randomly routing thecaller to any logged-in agent. This reporting method can also bemodified to compare the optimal interaction chance of a specific agentrouting against the chances of an optimal interaction as averaged overall available agents or over all logged-in agents since the commencementof a particular campaign. In fact, by dividing the average chance of anoptimal interaction over all unavailable agents at a specific period oftime by the average chance of an optimal interaction over all availableagents at that same time, a report can be generated that indicates theoverall boost created by embodiments of the present disclosure to thechance of an optimal interaction at that time. Alternatively,embodiments of the present disclosure can be monitored, and reportsgenerated, by cycling embodiments of the present disclosure on and offfor a single agent or group of agents over a period of time, andmeasuring the actual contact results. In this manner, it can bedetermined what the actual, measured benefits are created by employingembodiments of the present disclosure.

As noted, embodiments of the present disclosure can include a visualcomputer interface and printable reports provided to the contact centeror their clients to allow them to, in a real-time or a past performancebasis, monitor the statistics of agent to caller matches, measure theoptimal interactions that are being achieved versus the interactionspredicted by the computer model, as well as any other measurements ofreal time or past performance using the methods described herein. Avisual computer interface for changing the weighting on an optimalinteraction can also be provided to the contact center or the contactcenter client, such that they can, as discussed herein, monitor orchange the weightings in real time or at a predetermined time in thefuture.

Call Pattern Sensitivity:

It has been discovered that call pattern sensitivity to agentperformance may be used successfully for matching calls and agents withsuperior results. In embodiments, call pattern performance for a givencall pattern may be correlated to agent performance in an historicaldata set for that given call pattern and/or obtained via inference fromother performance data for other call patterns. The pattern performancesensitivity correlation to agent performance may then be used in amatching algorithm. In embodiments, Spearman or Pearson correlation maybe used. In embodiments the matching engine may compute an (Bayesianregression) estimate of the difference in the call pattern performancebetween the top performing half of agents and the bottom performing halfof agents. In embodiments, the call pattern performance data for goodagents, for example an average call pattern sales rate for agents in thetop half of agent performance rankings can be subtracted or otherwisecompared to an average call pattern sales rate for agents in the bottomhalf of agent performance. Thus, for example, call patterns withperformance that are most highly correlated to performance of agentsmay, in embodiments, be matched to agents with highest performancepercentiles or rankings.

In embodiments, call pattern sensitivity to agent performance may becalculated by:

1) Determining for the top half of ranked or percentiled agents in thegroup of agents handling this call pattern in the training data set anaverage underlying performance;

2) Determining for the bottom half of the ranked or percentiled agentsin the group of agents handling this call pattern in the training dataset an average underlying performance;

3) Subtracting the bottom half agents average underlying performancefrom the top half agents average underlying performance for this callpattern to obtain a performance difference number;

4) Comparing the performance difference number for this particular callpattern to a predetermined number or a sensitivity number determined onthe fly; and

5) Ranking or percentiling this sensitivity number for the call patternsin the set of call patterns.

In embodiments, call pattern sensitivity to agent performance may becalculated by:

1) Estimating by Bayesian methods, the top half agents averageunderlying performance minus the bottom half agents average underlyingperformance for this particular call pattern to obtain a performancedifference number;

2) Comparing the performance difference number for this particular callpattern to the performance difference number for other call patterns;and

3) Ranking or percentiling this performance difference number for thecall patterns in the set of call patterns.

Note that embodiments of the disclosure are not limited to a particularalgorithm to be used to determine the sensitivity. Note that the agentsin the set of agents may be grouped in more than two groups. Note thatanother number may be used in place of or in addition to the average.Note that in embodiments, percentiles or rankings may be used formatching, or raw or modified performance data and raw or modifiedsensitivity ratings may be used for matching. For example, a percentileof 75 for a call sensitivity agent performance may be matched with anagent with a percentile of 75 for agent performance. Note that it isunderstood by one of skill in the art that this means that matching isbased at least in part on rankings, or matching is based at least inpart on percentiles, but both rankings and percentiles are not used atthe same time.

Referring to FIG. 7, an embodiment of a method of operating a callcenter system is disclosed wherein call sensitivity to agent performanceis used in a matching operation. Referring to the figure, block 700represents an operation of obtaining for each call in a set of calls, bythe one or more computers, a respective pattern representing one ormultiple different respective data fields. In embodiments, thisoperation may comprise the operation of obtaining, by one or morecomputers, a respective data stream for each of multiple calls, witheach respective data stream having one or more different locations alongthe data stream representing one or more different respective fields. Inembodiments, the data stream may be abstracted in whole or in part, sothat the meaning for the field data in the respective differentlocations for the different respective fields is not known by thesystem. In embodiments, this operation may further comprise determiningrespective patterns, by the one or more computers, from respective fielddata positions in the respective data streams. In embodiments, thesepatterns may be patterns of 1's and 0's, or yes and no, or true andfalse, or binary, or integer only, or decimal, or other alphanumericrepresentations for the different fields. In embodiments, all of some ofthis data may comprise human interpretable field data. In embodiments,only selected positions in the data stream may be used to form the callpattern. In embodiments, all of the positions in the data stream may beused to form the call pattern. In embodiments, some of the data fieldsmay be known, but some of the data fields may not be known. Inembodiments, a data abstraction process may be performed by the presentsystem. Embodiments incorporating abstract data are described in moredetail in application Ser. No. 14/032,657 filed Sep. 20, 2013, “Use ofAbstracted Data in Pattern Matching System,” incorporated by referenceinto this application.

Block 710 represents an operation of obtaining, by the one or morecomputers, performance data for the respective patterns of the calls. Inembodiments, this database may be obtained from the one or moredatabases. In embodiments, a large training data set may be obtained oraccessed by a pattern matching system and used to determine correlationsof the different call patterns to desired results actually obtained forthese particular call patterns, e.g., a sale, retention of the caller ina program, call handle time, customer satisfaction, revenue, first callresolution, units sold, and transaction points, to name just a few. Forexample, it may be determined from the training data that the callpattern 100110100100 of field data correlates to low sales potential,while the call pattern 001110110100 of field data correlates to highsales potential.

In embodiments, the operation of block 710 may comprise percentiling orranking, e.g., computing a percentile of 0 to 100 or ranking across theset, by the one or more computers, the respective patterns based atleast in part on pattern performance sensitivity to agent performance,where pattern performance sensitivity is defined as pattern performancecorrelated to agent performance data. In other words, a determination ismade relative to other of the call patterns in a set of call patterns,whether there is data in the training set indicating that the respectivecall pattern highly correlates with agent performance, e.g., there ishigh performance for most of the calls with this pattern when the agentshandling the respective calls have high performance ratings, and/or lowperformance for most of the calls with this pattern when the agentshandling the respective calls have low performance ratings.Alternatively, a low call pattern correlation with agent performancewould be indicated where the call pattern performance does not seem tosubstantially track with the level of agent performance. Patternperformance may be inferred based at least in part on the historicaldata in the one or more databases for this pattern or for other patternsthat correlate to this pattern.

In embodiments, instead of or in addition to determining a percentile orranking within a set of call patterns of the respective pattern forsensitivity to agent performance, the sensitivity data/rating for therespective pattern may be compared to one or more thresholds, or may beused with or without modification.

Block 720 represents an operation of obtaining, by the one or morecomputers, performance data for respective of the agents in a set ofagents. In embodiments, the performance may be obtained from the one ormore databases. In embodiments, this operation may comprise percentilingor ranking, by the one or more computers, agents in a set of agentsbased at least in part on agent performance. In embodiments, these agentperformance ratings may be based at least in part on historical callresult data in a historical data set for the respective agent and/or maybe inferred in whole or in part from demographic data or personalitydata for the agent as compared to other agents with similar data andtheir outcome results. Desired performance outcomes in the historicaltraining data set on which agents may be rated comprise sales, retentionof the caller in a program, call handle time, customer satisfaction,revenue, first call resolution, units sold, and transaction points, toname just a few. In embodiments, instead of or in addition todetermining a percentile or ranking of the respective agent within a setof agents, the performance data/rating for the respective agent may becompared to one or more thresholds, or may be used with or withoutmodification.

Block 730 represents an operation of determining, by the one or morecomputers, pattern performance sensitivity to agent performancecomprising the pattern performance data correlated to the agentperformance data. In embodiments, the determining pattern performancesensitivity to agent performance comprises performing the operation ofcorrelating call performance data in the data set to agent performanceof the agents handling the calls in a data set. In embodiment, acomputation may be performed to obtain a percentile or ranking of thecall performance sensitivity to agent performance within a set of calls.

Block 740 represents an operation of matching, by the one or morecomputers, a respective one of the agents from the set of agents to oneof the calls based at least in part on the performance data for the oneagent and on the pattern performance sensitivity to agent performancefor the respective call. In embodiments where percentile or rankingshave been obtained, the matching may be based at least in part on thepercentile or ranking for the one agent from the set of agents and onthe percentile or ranking by pattern performance sensitivity to agentperformance for the pattern of the respective call. Alternatively, inembodiments the matching may be based at least in part on the raw ormodified performance data/rating for the agent and/or the raw ormodified sensitivity rating for the call pattern.

In embodiments, to avoid skewed agent utilization, agent ratings orpercentiles or rankings may be weighted based at least in part on callhandle time. In embodiments, the weighting may be based on average ormean handle time for the agent and/or weighted by the average number ofcalls handled by the agent relative to a system wide call number or thecall handle time for the agent relative to a system wide call handletime number. Thus, in embodiments, the width of a given agent's domainmay be weighted by the number of calls the agent handles in a givenperiod relative to the total number of calls for available agents, or bythe total or average handle time, e.g., total minutes, for the agentduring the period, relative to a total estimated handle time for callsin a set of calls or an average handle time for the calls in the set ofcalls.

In embodiments, a weighting algorithm may comprise:

1. Agents may be ranked or percentiled in some manner in order of eitherperformance, or sensitivity of performance to call pattern performance,average handle time, or etc.

2. A weight may then be assigned to each agent equal to either theagent's estimated percentage of total number of calls received, e.g.,the call count, for a time period, or the agent's estimated percentageof call handle time relative to a total time for calls handled by thecall center during the time period for which the matching is beingperformed, or some combination thereof. Note that in embodiments, thecall count data and/or the call handle time data for the agents that arenot logged in may be removed from the call center call total or the callcenter call handle time total.

3. For example, for percentiling, the percentile assigned to the lowestranked agent may be one half the weight assigned in (2) above.

4. The percentile assigned to the second lowest ranked agent may be thesum of the weight assigned to the lowest agent plus one half the weightassigned to the second lowest agent.

5. The percentile assigned to the third lowest ranked agent may be thesum of the weights assigned to the two lowest agents plus one half theweight assigned to the third lowest agent.

6. Etc.

An example of this weighting based at least in part on call counts andusing percentiles, for 11 agents and 1100 calls, where a 5^(th) agentthat has taken 100 calls is not logged in would be: Lowest rated agenthas taken 200 calls=20% or a weight of 20, resulting in 10 percentile.(Note that the total call center call count for the calculation is1100-100 because the agent not logged in had 100 calls.)

2^(nd) Lowest agent takes 100 calls=10% or a percentile of 20+5=25

3^(rd) lowest agent takes 50 calls=5% or a percentile of 20+10+2.5=32.5.

4^(th) lowest agent takes 100 calls=10% or a percentile of 20+10+5+5=40.

Etc.

Various other weighting schemes for weighting agent performance or agentsensitivity to call performance, where the weighting may be used toprevent skewing.

Another example of a weighting scheme may comprise setting thepercentile for each agent evenly in increments with the incrementsdetermined based at least in part on the number of agents in the set ofagents, e.g., for 10 agents, increment the percentile by 10 from agentto agent. For example,

Lowest agent in performance=5 PERCENTILE

2nd lowest in performance=15 PERCENTILE

3rd lowest in performance=25 PERCENTILE

3rd lowest in performance=35 PERCENTILE

4rd lowest in performance=45 PERCENTILE

5rd lowest in performance=55 PERCENTILE

6th lowest in performance=65 PERCENTILE

Etc.

Note that the weighting examples above may be applied in embodiments tocalls and call sensitivity to agent performance. Thus in embodiments, toavoid skewed call pattern utilization of resources, call patternperformance and/or call pattern performance sensitivity to agentperformance ratings or percentiles or rankings may be weighted based atleast in part on call count for calls for the given pattern relative toa system wide call number or estimated call handle time for calls withthis pattern relative to a system wide handle time number. Inembodiments, the weighting may be based on average or mean handle timefor the call pattern and/or weighted by the average number of calls withthis pattern relative to a total number of calls in the set of calls(e.g., calls in the system at that time or in a given period) orrelative to an average number of calls for the system. In embodiments,the weighting may be by the estimated call handle time for the callpattern relative to an estimated total call handle time for calls in theset of calls or an average call handle time for calls in the set ofcalls. Thus, in embodiments, the width of a given call pattern's domainmay be weighted by the number of calls with this pattern in a givenperiod relative to the total number of calls in the set of calls, or bythe total or average handle time for calls with this pattern, e.g.,total minutes during the period, relative to a total estimated handletime for calls in the set of calls or an average handle time for thecalls in the set of calls.

Thus in embodiments, the weighting of percentiles or rankings may bebased at least in part on a call count for each of the patterns in a setof calls relative to a total number of calls in the call set or anaverage number of calls per pattern in the call set. Likewise, weightingmay be based at least in part on weighting by an estimated call handletime for the call relative to a total call handle time or an averagecall handle time for calls in the set of calls.

Likewise, in embodiments the percentiles or rankings of callperformances and/or call pattern sensitivity may be weighted evenly inincrements, with the increments determined based at least in part on thenumber of calls in the call set.

Accordingly, in embodiments the method may further comprise theoperation of weighting, by the one or more computers, the ranking orpercentile of agent performance and/or weighting call performance and/orcall sensitivity to agent performance by one or more parameters.

In embodiments where the weighting is of agent performance, theweighting may be based at least in part on a call count for therespective agent relative to a total or adjusted call count for the callcenter during a period. Alternatively or in addition, the weighting maybe based at least in part on a call handle time total or an averagehandle time for the respective agent relative to a total or adjustedcall handle time for the call center during a period.

In embodiments where the weighting is of call performance, the weightingmay be based at least in part on an estimated call handle time for therespective call relative to the call handle times for the set of calls.

Note that embodiments of the disclosure may comprise weighting agentperformance and/or call performance in a matching scheme where callsensitivity to agent performance or agent sensitivity to callperformance are not used in the matching algorithm. For example, thematching algorithm may comprise matching the weighted rankings orpercentiles of agents to either the rankings or percentiles of the callsin a set of calls or to the weighted rankings or percentiles of thecalls. In other embodiments, the matching algorithm may comprisematching the weighted rankings or percentiles of calls to the rankingsor percentiles of the agents in a set of agents or to the weightedrankings or percentiles of the agents.

In embodiments, this weighting allows a distribution of the load. Notethat agent sensitivity to call performance may be weighted in the samemanner. As noted, alternatively or in addition, the weighting may bebased on one or more other parameters, such as call handle time. Inembodiments, edge correction weighting may also be applied, as disclosedin application Ser. No. 13/843,724 filed on Mar. 15, 2013 and Ser. No.13/843,541 filed on Mar. 15, 2013.

In embodiments, matching based at least in part on call sensitivity toagent performance may be implemented using a system as shown in FIG. 11.Referring to the figure, calls are obtained, e.g., either received orgenerated in a call exchange component 1100. A call data extractor andpattern generator 1110 may be used to extract call data from ahistorical call database 1120 to obtain a call pattern comprising one ormore data fields of call data. The historical call database 1120 mayinclude demographic data and/or psychographic data for the caller, skillrequirements for previous calls, and outcomes of previous calls, to namea few of the items that may be available. This component may alsoextract call data from other databases and/or generate call data on thefly based on the call number or other call data.

A call pattern performance extractor component 1130 may be connected toreceive the call pattern from the block 1110 to obtain performance datafor the respective call pattern from an historical call database, or toinfer it from similar call patterns. In embodiments, the callperformance extractor component 1130 may also perform a percentile orranking operation to compute a percentile from 0-100 or to rank thepattern performance of the call against other calls in a set of calls.

Likewise, an agent data and performance extractor component 1150 may beconnected to extract data from an agent database 1140 that containsdemographic data and/or psychographic data and skill data andperformance data for agents. In embodiments, the extractor component1150 may be configured to obtain agent data for agents in a set ofagents, e.g., agents that are currently available, or agents that arecurrently available or are soon to be available, or any other convenientset of agents selected based at least in part on one or more criteria,e.g., such as skill and/or call handle time, to name a few. Inembodiments, the component 1150 may perform a percentile or rankingoperation relative to agents to compute a percentile or ranking within aset of agents based at least in part on performance data for the agents.

In embodiments, a call sensitivity to agent performance correlationengine 1160 may receive inputs from the call pattern performanceextractor 1130 and from the agent data and performance extractorcomponent 1150. The correlation engine 1160 may perform the correlationof the performance for a selected call pattern to agent performance foragents that handled this call pattern in the past. In embodiments, thecalls in a set of calls may be percentiled or ranked by call sensitivityto agent performance.

In embodiments, a matching engine 1170 performs matching of the givencall to an agent based as at least in part by selecting an agent basedat least in part on the agent performance data and based at least inpart on the call pattern sensitivity to agent performance. Theparticular matching may be high call pattern sensitivity matched toagents with high performance data, or any other matching criteria thatmay factor in other data elements such as skill, handle time, call type,etc. In embodiments, the matching may be based at least in part onmatching percentiles or rankings of the call pattern sensitivity withinthe set of calls and the agent performance within the set of agents.

In embodiments, a switching between or among algorithms has beendiscovered to be advantageous. In some embodiments, this is due tosecond order effects from estimating differentials in call patternperformance between good performance calls and bad performance callsand/or differential noise effects from estimating differentials in agentperformance between good agent performance and bad agent performance.

In embodiments, the calls in a set of calls may be grouped into two ormore groups based on one or more criteria. In embodiments, one of thegroups may be matched using a first algorithm, and a different group maybe matched using a second algorithm.

In embodiments, the groupings may be based at least in part onrespective estimated performances of the patterns for a set of calls. Inembodiments, the call patterns may be percentiled or ranked by patternperformance, and then grouped by their percentiles or rankings into twoor more groups, comprising one group with percentiles or rankings in onerange of percentiles or rankings, and at least a different group thathas percentiles or rankings in a higher range relative to the one group,e.g., at least a top group and a bottom group, or a top group and amiddle group and a low group, etc. The number of groupings is notlimiting on embodiments of the present disclosure. Note that thegrouping do not have to have equal numbers in each group. For example,25% of the call patterns could be in the low group, and 75% of the callpatterns could be in the higher group, or vice versa. Other examples,would be 50-50 groupings where two groups are used, or any othergrouping ratio. The percentages of the call set in the groups is notlimiting on embodiments of the present disclosure and may be determinedbased on one or more criteria. For example, the ratio of calls in thegroups may be determined empirically, and/or may be based at least inpart on historical data in the one or more databases. Alternatively, inembodiments the ratios may be based at least in part on different rangesof raw or modified pattern performance data, and/or based on dataavailability or any other criteria.

In embodiments, call patterns in the lowest group of performancepercentiles or rankings may be matched using the first algorithm, whilecalls in the different group of performance percentiles or rankings maybe matched using the second algorithm. For example, in embodiments callpatterns in the lowest group may be matched using an algorithm thatmatches based at least in part on percentiles or rankings of callpattern performance to agent percentiles or ranking by performance. Inthis embodiment, call performance percentiles or rankings in the lowperformance group may be matched with agents with low performancepercentiles or rankings. Alternatively, calls in this low call patternperformance group may be matched with agents with higher performancepercentiles or rankings.

In embodiments, call patterns in a higher group of performancepercentiles or rankings may be matched using a different algorithm thatmatches based at least in part on call pattern sensitivity to agentperformance and agent performance. For example, calls in this highergroup of call patterns may be matched so that agents with higherperformance percentiles or rankings are matched with call patternpercentiles or rankings that are more highly influenced by good agentperformance.

Thus, in embodiments, for this higher performance group of callpatterns, instead of matching calls with the highest performancepercentiles or rankings with agents with the highest performancepercentiles or rankings, matching is performed by determining arespective call pattern's sensitivity to agent performance, e.g.,percentiles or rankings of calls is made based at least in part on acorrelation between the call pattern performance and agent performance.Thus, high performance agents are not wasted on call patterns whereagent performance has a low influence on the outcome for the call. Thesehigh performance agents are instead matched with call patterns whereagent performance is expected to have a strong influence. In essence,the high performing agents are reserved (to prevent over-use) andmatched to call patterns where they will be most useful. This operationreduces a situation where a good agent is not available for a callpattern that is highly influenceable by agent performance. Poorly rankedagents are matched where their low performance percentile or rankingwill have the least adverse effect.

More generically, in other embodiments, a group of calls that aresimilar in one or more aspects, may be grouped, and one algorithm may beused with this group, and a different algorithm may be used with adifferent group having different characteristics. For example, onealgorithm may be used where the system has a lot of current data for apattern. A different algorithm may be used where there is not a lot ofdata for the pattern in the system. Thus, to facilitate this operationusing multiple algorithms, the patterns may be grouped in multiplegroups based on different amount ranges of current data for therespective patterns. Alternatively or in addition, in embodimentsgrouping may be based at least in part on percentiles or rankings orratings of the skills needed by the calls, and/or on the proficiency ofthe agents for a given skill. In such embodiments, the groupings may below range and higher range groups of skill proficiency, or a low range,medium range, and high range skill proficiency, or any other number ofgroups based on skill proficiency ranges. Different matching algorithmsmay be used for the different groups. Alternatively or in addition, inembodiments grouping may be based at least in part on agent call handletime, or on estimated call handle time for a given call pattern. In suchembodiments, the groupings may be low range and higher range groups ofagent call handle time or pattern call handle time, or low range, mediumrange, and high range groups of agent call handle time or pattern callhandle time, or any number of groups based on agent call handle time orpattern call handle time ranges. As noted, different matching algorithmsmay be used for the different groups. Thus, a variety of differentgroupings are contemplated. In embodiments, a cutoff or breakpointbetween the different groups may be determined empirically or using oneor more criteria.

In embodiments, a criterion for the cutoff or breakpoint between thegroups may be a statistical criterion based on confidence intervalestimates. For example, if the data supporting performance determinationin the historical or other databases is substantial while the datasupporting call sensitivity correlation to agent performance is notsubstantial for the call patterns in the set of calls, then thebreakpoint may be skewed so that more of the calls are handled in agroup that matches based on call performance and agent performance.Likewise, if the data supporting performance determination in thehistorical or other databases is not substantial while the datasupporting call sensitivity correlations to agent performance issubstantial for the call patterns in the set of calls, then thebreakpoint may be skewed so that more of the calls are handled in agroup that matches based on call sensitivity to agent performance andagent performance.

Referring to FIG. 8, embodiments using two different matching algorithmsare disclosed wherein at least one of the algorithms uses call patternsensitivity to agent performance. Block 800 represents an operation ofobtaining, by the one or more computers, for each call in one set ofcalls, a respective pattern representing one or multiple differentrespective data fields.

In embodiments, this operation may comprise an operation of obtaining arespective data stream for each of multiple calls, with each respectivedata stream having one or more different locations along the data streamrepresenting one or more different respective fields. In embodiments,the data stream may be abstracted in whole or in part, so that themeaning for the field data in the respective different locations is notknown by the system. The operation may further comprise determiningrespective patterns, by the one or more computers, from respective fielddata positions in the respective data streams. In embodiments, thesepatterns may be patterns of 1's and 0's, or yes and no, or true andfalse, or binary, or integer only, or decimal, or other alphanumericrepresentations for the different fields. In embodiments, all or some ofthis data may comprise human interpretable field data. In embodiments,all of the positions in the data stream are used to form the respectivepattern. In embodiments, only selected positions in the data stream areused to form the respective pattern.

Block 810 represents an operation of obtaining, by the one or morecomputers, performance data for the respective patterns of the calls. Inembodiments, this data may be obtained from the one or more databases.In embodiments, this operation may comprise percentiling or ranking, bythe one or more computers, the respective patterns of the calls based atleast in part on pattern performance for the respective patterns.Pattern performance for one or more desired outcomes may be inferredbased at least in part on the historical data in the one or moredatabases, e.g., training data, for this pattern or for other patternsthat correlate to this pattern. As noted above, in embodiments, insteadof or in addition to determining a percentile or ranking of therespective pattern within a set of call patterns, the performancedata/rating for the respective call pattern may be compared to one ormore thresholds, or may be used with or without modification.

Block 820 represents an operation of obtaining, by the one or morecomputers, performance data for respective of the agents in a set ofagents. In embodiments, the performance may be obtained from the one ormore databases. In embodiments, this operation may comprise percentilingor ranking, by the one or more computers, agents in a set of agentsbased at least in part on agent performance.

Block 830 represents an operation of determining, by the one or morecomputers, pattern performance sensitivity to agent performancecomprising the pattern performance data correlated to agent performancedata. In embodiments, the determining pattern performance sensitivity toagent performance operation may comprise the step of correlating callperformance data in the data set to agent performance of the agentshandling the calls in the data set. In embodiments, a further operationmay be performed of percentiling or ranking, by the one or morecomputers, the respective patterns of the calls based at least in parton their respective pattern performance sensitivity to agentperformance. Desired performance outcomes in the training data set onwhich patterns may be rated comprise sales, retention of the caller in aprogram, call handle time, customer satisfaction, revenue, first callresolution, units sold, and transaction points, to name just a few. Inembodiments, instead of or in addition to determining a percentile orranking within a set of call patterns of the respective pattern forsensitivity to agent performance, the sensitivity data/rating for therespective pattern may be compared to one or more thresholds, or may beused with or without modification.

Block 830 represents an operation of obtaining, by the one or morecomputers, performance data for agents in a set of agents. As noted, inembodiments this data may be obtained from the one or more databases. Inembodiments, this operation may comprise percentiling or ranking, by theone or more computers, agents in a set of agents based at least in parton agent performance. In embodiments, these agent performance ratingsmay be based at least in part on historical call result data for therespective agent and/or may be inferred in whole or in part fromdemographic data or personality data for the agent and other agents withsimilar data and their outcome results. Desired performance outcomes inthe training data set on which agents may be rated comprise sales,retention of the caller in a program, call handle time, customersatisfaction, revenue, first call resolution, units sold, andtransaction points, to name just a few. As noted above, in embodiments,instead or in addition to determining a percentile or ranking of therespective agent within a set of agent, the performance data/rating forthe respective agent may be compared to one or more thresholds, or maybe used with or without modification.

Block 840 represents an operation of grouping, by the one or morecomputers, the patterns for the one set of calls into at least twogroups comprising one group and different group based one or morecriteria. In embodiments, the grouping may be based at least in part onperformance data for the respective patterns. In embodiments, thegrouping may be based at least in part on the performance percentiles orrankings of the call patterns within the set of calls, with the callpatterns in one of the groups having a lower percentiles or rankingrange than call patterns in the different group. In embodiments, theremay be two groups, e.g., a low performance range group and a highperformance range group, or three groups, or four groups, or more.Alternatively, the groupings may be based at least in part on differentranges of raw or modified pattern performance data. The number of groupsis not limiting on embodiments of the present disclosure. Ratio exampleswhere two groups are used would be grouping ratios of 25-75, and 50-50,75-25, or any other grouping ratio. The percentages of the call set inthe respective groups is not limiting on embodiments of the presentdisclosure and may be determined based on one or more criteria. Forexample, the ratio of calls in the groups may be determined empirically,and/or may be based at least in part on historical data in the one ormore databases and/or on the availability of data for the parameter,and/or other parameters.

In other embodiments, the groupings may be based at least in part onagent performance ranges. In other embodiments, the groupings may bebased at least in part on data volume ranges for the respective callpatterns, e.g., how much data is available in the training data set forthe respective call patterns.

Block 850 represents an operation of matching for the one group of thecalls, by the one or more computers, a respective one of the agents fromthe set of agents to one of the calls in the one group using a firstalgorithm. In embodiments, the first algorithm for matching of arespective one of the agents from the set of agents to one of the callsin the one group may be based at least in part on the performance datafor the one agent and the performance data for the pattern of the onecall in the one group. In embodiments, the matching of the one group ofcalls may be based at least in part on the agent percentile or rankingby performance of the respective one agent and the call percentile orranking by performance of the respective pattern for the one call in theone group. For example, the matching step for the one group may comprisematching call patterns in a low percentile or ranking group based atleast in part on the percentiles or rankings for the agents and thepercentiles or rankings by performance data for the respective callpatterns in the one group. Alternatively, the groups and the matchingmay be reversed. As noted, in embodiments, the matching may instead orin addition be based at least in part on the raw or modified performancedata/ratings for the agent and/or the call pattern.

Block 860 represents an operation of matching for the different group ofthe calls, by the one or more computers, a different agent from the setof agents to one of the calls in the different group using a secondalgorithm. In embodiments, the matching of a different agent from theset of agents to one of the calls in the different group may be based atleast in part on the performance data for the different agent and thepattern performance sensitivity to agent performance for the respectivepattern for the call in the different group. In embodiments, thematching for the different group of the calls may be based at least inpart on the agent percentile or ranking by the performance of therespective different agent and the call percentile or ranking by patternperformance sensitivity to agent performance for the respective patternfor the call in the different group. Thus, in embodiments, the matchingstep for the different group may comprise matching call patterns in ahigher percentile or ranking group based at least in part on percentilesor rankings by performance for the agents and percentiles or rankings bypattern performance sensitivity to agent performance in this differentgroup. Alternatively, the groups and the matching may be reversed. Inembodiments, the matching may be based at least in part on the raw ormodified performance data/ratings for the agents and/or the raw ormodified sensitivity rating for the call pattern.

In embodiments, these matching steps may be performed concurrently. Inembodiments, the matching steps may be performed consecutively or mayhave overlapping performance. In embodiments, a percentile or rankingbreakpoint between the one group and the different group may bedetermined based on one or more criteria. As noted, one criterion may beto determine the percentile or ranking cutoff point empirically, and/ormay be based at least in part on historical performance data in the oneor more databases for various percentile or ranking cutoff points orbased at least in part on a level of data availability for the agent orthe call pattern, or one or more other criteria.

As noted, in embodiments a criterion for the cutoff or breakpointbetween the groups may be a statistical criterion based on confidenceinterval estimates. For example, if the data supporting performancedetermination in the historical or other databases is substantial whilethe data supporting the agent sensitivity to call performancecorrelation is not substantial for the agents in the set of agents, thenthe breakpoint may be skewed so that more of the agents are assigned toa group that matches based on call performance and agent performance.Likewise, if the data supporting performance determination in thehistorical or other databases is not substantial while the data thesupporting agent sensitivity to call performance correlation issubstantial for the agents in the set of agents, then the breakpoint maybe skewed so that more of the agents are in a group that matches basedon agent sensitivity to call performance and call performance.

In embodiments, a further operation may be performed of matching, by theone or more computers, a different set of calls to agents using adifferent matching algorithm. For example, the different algorithm maybe a random matching based at least in part on call position in a queueor length of time of a call in a pool of callers. The further operationmay then be performed of comparing performance data from call-agentmatches of the different set of calls using the different matchingalgorithm with performance data for the one set of calls using acombination of the matching using the first algorithm of the one groupof call patterns to agents and the matching using the second algorithmof the different group of call patterns to agents. In embodiments, anoperation may also be performed of generating, by the one or morecomputers, a report or display data for the performance comparingresults from using the combination of matching algorithms againstperformance using the different matching algorithm.

In embodiments, matching based at least in part on switching between twoor more matching algorithms that use grouping and are based at least inpart on call sensitivity to agent performance may be implemented using asystem as shown in FIG. 12. Referring to the figure, calls are obtained,e.g., either received or generated in a call exchange component 1200. Acall data extractor and pattern generator 1210 may be used to extractcall data from a historical call database 1220 to obtain a call patterncomprising one or more data fields of call data. The historical calldatabase 1220 may include demographic data and/or psychographic data forthe caller, skill requirements for this or previous calls, and outcomesof previous calls, to name a few of the items that may be available.This component may also extract call data from other databases and/orgenerate call data on the fly based on the call number or other calldata.

A call pattern performance extractor component 1230 may be connected toreceive the call pattern from the block 1210 and to obtain performancedata for the respective call pattern from the historical call database1220 or to infer it from similar call patterns. In embodiments, the callperformance extractor component 1230 may also perform a percentile orranking operation to compute a percentile or to rank the patternperformance for the call against other calls in a set of calls.

Likewise, an agent data and performance extractor component 1250 may beconnected to extract data from an agent database 1240 that containsdemographic data and/or psychographic data and skill data andperformance data for agents. In embodiments, the agent data andperformance extractor component 1250 may be configured to obtain agentdata for agents in a set of agents, e.g., agents that are currentlyavailable, or agents that are currently available or are soon to beavailable, or any other convenient set of agents selected based at leastin part on one or more criteria such as skill and/or call handle time,to name a few. In embodiments, the agent data and performance extractorcomponent 1250 may perform a percentile or ranking operation to computea percentile or rank the agents within a set of agents based at least inpart on performance data for the agents.

In embodiments, a call sensitivity to agent performance correlationengine 1260 may receive inputs from the call pattern performanceextractor 1230 and from the agent data and performance extractorcomponent 1250, and generate a correlation of the performance for aselected call pattern to agent performance for agents that handled thiscall pattern in the past. In embodiments, a computation may be performedto percentile or rank the calls in a set of calls by call sensitivity toagent performance.

In embodiments, a grouping engine 1270 may be provided to group thecalls or the agents into two or more groups based on one or morecriteria. In embodiments, the grouping may be based at least in part oncall performance ranges, as previously described. In other embodiments,the groupings may be based at least in part on volume ranges of data forthe patterns, ranges of call handle time, skill level ranges, agentperformance ranges, to name a few. The grouping engine 1270 is shown inthe figure as receiving inputs from any of blocks 1230, 1250, 1260,and/or potentially may also receive an input from block 1210, dependingon the one of more parameters used for the grouping.

In embodiments, a matching engine 1280 using one algorithm may be usedfor one group of calls. The one algorithm may be configured for matchingone of the agents from the set of agents to one of the calls in the onegroup based at least in part on the performance data for the one agentand the performance data for the pattern of the one call in the onegroup. In embodiments, the matching may be based at least in part onmatching percentiles or rankings of the call pattern performance withinthe set of calls and the percentiles or rankings of the agentperformance within the one group of agents. Block 1280 illustrates anembodiment where inputs are taken from blocks 1230 and 1250.

In embodiments, a matching engine 1290 using a different algorithm maybe used for calls in a different group of the calls. The matching engine1290 may be configured for matching a different agent to one of thecalls in the different group of calls based at least in part on theagent performance data for the different agent and based at least inpart on the call pattern sensitivity to agent performance for therespective pattern for the call in the different group. The particularmatching may be high call pattern sensitivity matched to agents withhigh performance data, or any other matching criteria that may factor inother data elements such as skill, handle time, call type, etc. Inembodiments, the matching for this different group may be based at leastin part on matching percentiles or rankings of the call patternsensitivity within the set of calls and the agent performance within theset of agents. Block 1290 illustrates an embodiment where inputs aretaken from blocks 1260 and 1250.

Agent Sensitivity to Call Performance:

For some agents, it has been discovered that there is a dramaticcorrelation of their agent performance to call performance. In someagents, there is minimal correlation. The agent sensitivity to callperformance correlation may be used in a matching algorithm. Thus, forexample, agents with performance that highly correlates to performanceof call patterns may be matched to call patterns with a high performancepercentiles or rankings. Note that instead of percentiles or rankings,raw or modified performance data and/or raw or modified agentsensitivity ratings may be used for matching.

Referring to FIG. 9, embodiments are disclosed for a method of operatinga call center system with matching based at least in part on agentsensitivity to call performance. Referring to the figure, block 900represents an operation of obtaining for each call in a set of calls, bythe one or more computers, a respective pattern representing one ormultiple different respective data fields. In embodiments, thisoperation may comprise obtaining, by the one or more computers, arespective data stream for each of multiple calls, with each respectivedata stream having one or more different locations along the data streamrepresenting one or more different respective fields. In embodiments,the data stream may be abstracted in whole or in part, so that themeaning for the field data in the respective different locations for thedifferent respective fields is not known by the system. The operationmay further comprise determining respective patterns, by the one or morecomputers, from respective field data positions in the respective datastreams. In embodiments, these patterns may be patterns of 1's and 0's,or yes and no, or true and false, or binary, or integer only, ordecimal, or other alphanumeric representations for the different fields.In embodiments, all or some of this data may comprise humaninterpretable field data. In embodiments, all of the positions in thedata stream are used to form the respective pattern. In embodiments,only selected positions in the data stream are used to form therespective pattern.

Block 910 represents an operation of obtaining, by the one or morecomputers, performance data for the respective patterns of the calls. Inembodiments, this data may be obtained from the one or more databases.In embodiments, this step may comprise percentiling or ranking, e.g.,computing a percentile from 0 to 100 or ranking, by the one or morecomputers, the respective patterns of the calls in the set of callsbased at least in part on pattern performance for the respectivepatterns. Pattern performance for one or more desired outcomes may beinferred based at least in part on the historical data in the one ormore databases for this pattern or from other patterns that correlate tothis pattern. As noted, in embodiments, a large training data set may beobtained or accessed by a pattern matching system and used to determinecorrelations of the different call patterns to desired results actuallyobtained for these particular call patterns, e.g., a sale, retention ofthe caller in a program, call handle time, customer satisfaction,revenue, first call resolution, units sold, and transaction points, toname just a few. For example, it may be determined from the trainingdata that the call pattern 100110100100 correlates to low salespotential, while the call pattern 001110110100 correlates to high salespotential. As noted above, in embodiments, instead of or in addition todetermining a percentile or ranking of the respective pattern within aset of call patterns, the performance data/rating for the respectivecall pattern may be compared to one or more thresholds, or may be usedwith or without modification.

Block 920 represents an operation of obtaining, by the one or morecomputers, performance data for respective agents in a set of agents. Asnoted, this data may be obtained from the one or more databases. Asnoted, this operation may further comprise the operation of computing apercentile or ranking of the agents within the set of agents.

Block 930 represents an operation of determining, by the one or morecomputers, agent sensitivity to call performance for agents in a set ofagents comprising the agent performance data correlated to callperformance data for the calls the agent has handled. In embodiments,this operation may comprise the operation of correlating agentperformance data to call performance data for the calls the agent hashandled in a data set. In other words, a determination is made relativeto other of the agents within a set of agents, whether there is dataindicating that the respective agent performance highly correlates withcall performance, e.g., there is high performance for most of the callshandled by this agent when the calls he/she is handling have highperformance ratings, and/or low performance for most of the callshandled by the agent when the calls he/she is handling have lowperformance ratings. Alternatively, a low agent sensitivity orcorrelation with call performance would be indicated where the agentperformance does not seem to substantially track with the level of callperformance. In embodiments, this operation may also comprise acomputation of the percentile or ranking of the agent performancesensitivity to call performance within the set of agents. Inembodiments, instead of or in addition to determining a percentile orranking within a set of agents of the respective agent for sensitivityto call pattern performance, the sensitivity data/rating for therespective agent may be compared to one or more thresholds, or may beused with or without modification.

In embodiments, Spearman or Pearson correlation may be used. Inembodiments the matching engine may compute an (Bayesian regression)estimate of the difference in agent performance between the topperforming half of call patterns and the bottom performing half of callpatterns in the set of calls. In embodiments, the agent performance datafor good call patterns, for example an average agent sales rate for callpatterns in the top half of call pattern performance rankings can besubtracted or otherwise compared to an average agent sales rate for callpatterns in the bottom half of call pattern performance. Thus, forexample, agents with performance that are most highly correlated toperformance of call patterns may, in embodiments, be matched to callpatterns with highest performance percentiles or rankings.

In embodiments, agent sensitivity to call pattern performance may becalculated by:

1) Determining for the top half of ranked or percentiled call patternshandled by the particular agent in the training data set an averageunderlying performance;

2) Determining for the bottom half of the ranked or percentiled callpatterns handled by the particular agent in the training data set anaverage underlying performance;

3) Subtracting the bottom half call pattern average underlyingperformance from the top half call pattern average underlyingperformance for the particular agent to obtain a performance differencenumber;

4) Comparing the performance difference number for this particular agentto a predetermined number or a sensitivity number determined on the fly;and

5) Ranking or percentiling this sensitivity number for the agents in theset of agents.

In embodiments, agent sensitivity to call pattern performance may becalculated by:

1) Estimate by Bayesian methods, the top half call pattern averageunderlying performance minus the top half call pattern averageunderlying performance for the particular agent to obtain a performancedifference number;

2) Comparing the performance difference number for this particular agentto the performance difference number for other agents; and

3) Ranking or percentiling this performance difference number for theagent in the set of agents.

Note that embodiments of the present disclosure is not limited to aparticular algorithm to be used to determine the sensitivity. Note thatthe call patterns in the set of call patterns may be grouped in morethan two groups. Note that another number may be used in place of or inaddition to the average. Note that in embodiments, percentiles orrankings may be used for matching, or raw or modified performance dataand raw or modified sensitivity ratings may be used for matching.

Block 940 represents an operation of matching, by the one or morecomputers, a respective one of the agents from the set of agents to oneof the calls based at least in part on the performance data for therespective pattern of the one call and on the agent sensitivity to callperformance for the respective one agent of the set of agents. Inembodiments, this operation may comprise matching, by the one or morecomputers, a respective one of the agents from the set of agents to oneof the calls based at least in part on the percentile or ranking byperformance data for the respective pattern of the one call and on thepercentile or ranking of the agent sensitivity to call performance forthe respective one agent. In other embodiments, the matching may bebased at least in part on the raw or modified performance data/ratingfor the call pattern and/or the raw or modified sensitivity rating forthe agent.

In embodiments, matching based at least in part on agent sensitivity tocall performance may be implemented using a system as shown in FIG. 13.Referring to the figure, calls are obtained, e.g., either received orgenerated in a call exchange component 1300. A call data extractor andpattern generator 1310 may be used to extract call data from ahistorical call database 1320 to obtain a call pattern comprising one ormore data fields of call data. The historical call database 1320 mayinclude demographic data and/or psychographic data for the caller, skillrequirements for previous calls, and outcomes of previous calls, to namea few of the items that may be available. This component may alsoextract call data from other databases and/or generate call data on thefly based on the call number or other call data.

A call pattern performance extractor component 1330 may be connected toreceive the call pattern from the block 1310 and performance data fromthe historical call database 1320 to obtain performance data for arespective call pattern or to infer it from similar call patterns. Inembodiments, the call performance extractor component 1330 may perform apercentiling or ranking operation to rank the pattern performance of thecall against other calls in a set of calls.

Likewise, an agent data and performance extractor component 1350 may beconnected to extract data from an agent database 1340 that containsdemographic data and/or psychographic data and skill data andperformance data for agents. In embodiments, the extractor component1350 may be configured to obtain agent data for agents in a set ofagents, e.g., agents that are currently available, or agents that arecurrently available or are soon to be available, or any other convenientset of agents selected based at least in part on one or more criteria,e.g., such as skill and/or call handle time, to name a few. Inembodiments, the component 1350 may perform a percentiling or rankingoperation to compute a percentile or ranking of the agents within a setof agents based at least in part on performance data for the agents.

In embodiments, an agent sensitivity to call performance correlationengine 1360 may receive inputs from the call pattern performanceextractor component 1330 and from the agent data and performanceextractor component 1350. The correlation engine 1360 performs acorrelation of the performance for a selected agent in a set of agentsto call performance for the calls the agent has handled in the past. Inembodiments, this operation may comprise computing a percentile orranking of the agents in a set of agents by agent performancesensitivity to call performance.

In embodiments, a matching engine 1370 performs matching of a respectiveone of the agents from the set of agents to one of the calls based atleast in part on the performance data for the respective pattern of theone call and on the agent sensitivity to call performance for therespective one agent. The particular matching may be agents with highagent sensitivity to call performance matched to calls with highperformance data, or any other matching criteria that may factor inother data elements such as skill, handle time, call type, etc. Inembodiments, the matching may be based at least in part on matchingpercentiles or rankings of the agent sensitivity to call performancewithin the set of agents and the call performance within the set ofcalls.

In embodiments using agent sensitivity to call performance, it has beendiscovered that a switching between or among algorithms leads to betterperformance. In embodiments, the agents in a set of agents may begrouped into two or more groups based on one or more criteria. Inembodiments, the groupings may be based on ranges of agent performance.In embodiments, the respective estimated performances of the agents in aset of agents may be ranked, and then grouped by percentiles or rankingsinto two or more groups, comprising one group in one performance range,and at least a different group that has a higher performance rangerelative to the one group, e.g., at least a top group and a bottomgroup, or a top group and a middle group and a low group, etc.Alternatively or in addition, the groupings may be based at least inpart on ranges of raw or modified agent performance data. Note that thenumber of groups is not limiting on embodiments of the presentdisclosure. The groups do not have to have equal numbers in each group.For example, 25% of the agents could be in the low group percentile orranking range, and 75% of the agents could be in the higher group ofpercentile or ranking ranges. Other examples, would be 50-50 groupingsor 75-25 where two groups are used, or any other grouping ratio. Thepercentages of agents in the groups is not limiting on embodiments ofthe present disclosure and may be determined based on one or morecriteria. For example, the ratio of agents in the groups may bedetermined empirically, and/or may be based at least in part onhistorical data in the one or more databases. Alternatively, inembodiments the ratios may be based at least in part on different rangesof raw or modified pattern performance data, and/or based on dataavailability ranges or any other criteria.

In embodiments, one group of agents may be matched to calls using afirst algorithm, and a different group of the agents may be matched tocalls using a second algorithm. For example, in embodiments agents inthe lowest group of performance percentiles or rankings may be matchedusing an algorithm that matches based at least in part on call patternperformance percentile or ranking and agent performance percentile orranking. Low agent performance percentiles or rankings of the lowperforming agent group may be matched with calls with low performancepercentiles or rankings, e.g., 25% of the calls may be matched usingthis matching algorithm. Alternatively, agents in this low group withlow agent performance percentiles or rankings may be matched with callswith patterns with higher performance percentiles or rankings. Agents ina higher group of performance percentiles or rankings may be matchedusing a different algorithm that matches based at least in part on apercentile or ranking of agent sensitivity to call pattern performanceand call pattern performance percentile or ranking. For example, agentsin this higher performance group may be matched so that agents with highagent sensitivity to call pattern performance are matched with callpatterns that have a percentile or rank with a higher estimatedperformance. Alternatively, the matching may be based at least in parton raw or modified performance data.

Thus, in embodiments, for this higher performance group of agents,instead of matching agents with the highest performance percentiles orrankings with calls with the highest performance percentiles orrankings, matching may be performed by determining a respective agent'ssensitivity to call performance, e.g., a percentile or ranking of agentsbased at least in part on a correlation between the agent performanceand call pattern performance for the calls they have handled. Thus, highperformance calls are not wasted on agents where call performance has alow influence on agent performance and ultimately the outcome for thecall. These high performance calls are instead matched with agents wherecall pattern performance is expected to have a strong influence. Inessence, the high performing calls are reserved and matched to agentswhere they will be most useful. This operation reduces a situation wherea high performance call is not available for an agent that is highlyinfluenced by call performance. Poorly ranked calls are matched wheretheir low performance percentile or ranking will have the least adverseeffect.

More generically, in other embodiments, a group of agents that aresimilar in one or more aspects, may be grouped, and one algorithm may beused with this group, and a different algorithm may be used with adifferent group having different characteristics. For example, onealgorithm may be used where the system has a lot of current data for anagent. A different algorithm may be used where there is not a lot ofdata for the agent in the system. Thus, to facilitate this operationusing multiple algorithms, the patterns may be grouped in multiplegroups based on different amount ranges of current data for therespective agent. Alternatively or in addition, in embodiments groupingmay be based at least in part on percentiles or rankings or ratings ofthe skills needed by the calls, and/or on the proficiency of the agentsfor a given skill. In such embodiments, the groupings may be low rangeand higher range groups of skill proficiency, or a low range, mediumrange, and high range skill proficiency, or any other number of groupsbased on skill proficiency ranges. Different matching algorithms may beused for the different groups. Alternatively or in addition, inembodiments grouping may be based at least in part on ranges of agentcall handle time, or on ranges of estimated call handle time for callpatterns. In such embodiments, the groupings may be low range and higherrange groups of agent call handle time or pattern call handle time, orlow range, medium range, and high range groups of agent call handle timeor pattern call handle time, or any number of groups based on agent callhandle time or pattern call handle time ranges. As noted, differentmatching algorithms may be used for the different groups. Thus, avariety of different groupings are contemplated. In embodiments, acutoff between the different groups may be determined empirically orusing one or more other criteria.

Referring to FIG. 10, embodiments are illustrated using two differentmatching algorithms where at least one of the algorithms performsmatching based at least in part on agent sensitivity to callperformance. Block 1000 represents an operation of obtaining for each ofone set of calls, by the one or more computers, a respective patternrepresenting one or multiple different respective data fields. Inembodiments, this operation may comprise obtaining, by the one or morecomputers, a respective data stream for each of multiple calls, witheach respective data stream having one or more different locations alongthe data stream representing one or more different respective fields. Inembodiments, the data stream may be abstracted in whole or in part, sothat the meaning for the data fields in the respective differentlocations is not known by the system. In embodiments, the operation mayfurther comprise determining respective patterns, by the one or morecomputers, from respective field data positions in the respective datastreams. In embodiments, these patterns may be patterns of 1's and 0's,or yes and no, or true and false, or binary, or integer only, ordecimal, or other alphanumeric representations for the different fields.In embodiments, all or some of this data may comprise humaninterpretable field data. In embodiments, all of the positions in thedata stream are used to form the respective pattern. In embodiments,only selected positions in the data stream are used to form therespective pattern.

Block 1010 represents an operation of obtaining, by the one or morecomputers, performance data for the respective patterns of the calls. Inembodiments, this operation may comprise percentiling or ranking, by theone or more computers, the respective patterns of the calls within theset of calls based at least in part on performance data for therespective patterns. As noted above, in embodiments, instead of or inaddition to determining a percentile or ranking of the respectivepattern within a set of call patterns, the performance data/rating forthe respective call pattern may be compared to one or more thresholds,or may be used with or without modification.

Block 1020 represents an operation of obtaining, by the one or morecomputers, agent performance data for respective agents in a set ofagents. As noted, this data may be obtained from the one or moredatabases. In embodiments, this operation may comprise percentiling orranking, by the one or more computers, agents in a set of agents basedat least in part on agent performance. As noted above, in embodiments,instead of or in addition to determining a percentile or ranking of therespective agents within the set of agents, the performance data/ratingfor the respective agent may be compared to one or more thresholds, ormay be used with or without modification.

Block 1030 represents an operation of determining, by the one or morecomputers, agent performance sensitivity to call pattern performancecomprising the agent performance data correlated to pattern performancedata for calls the agent has handled. In embodiments, this operation maycomprise the operation of correlating agent performance to callperformance data for the calls the agent has handled in a data set. Inembodiments, this operation may comprise percentiling or ranking, by theone or more computers, agents in the set of agents based at least inpart on agent performance sensitivity to call pattern performancecomprising agent performance correlated to performance data for therespective pattern to obtain a respective agent percentile or rankingfor agent sensitivity to call performance for the respective agents. Inembodiments, instead of or in addition to determining a percentile orranking within a set of agents of the respective agent for sensitivityto call pattern performance, the sensitivity data/rating for therespective agent may be compared to one or more thresholds, or may beused with or without modification.

Block 1040 represents an operation of grouping, by the one or morecomputers, the set of agents into at least two groups comprising onegroup and a different group based on one or more criteria. Inembodiments, the grouping may be based at least in part on the agentperformance data. In embodiments, the groupings may be based at least inpart on the performance percentiles or rankings of the agents within theset of agents, with the agents in one of the groups having lowerpercentiles or rankings than agents in the other of the groups.Alternatively, the groupings may be based at least in part on differentranges of raw or modified agent performance data. The number of groupsis not limiting on embodiments of the present disclosure. For example,there may be two groups, e.g., a low performance range group and a highperformance range group, or three groups, or four groups, or more.Examples where two groups are used may have grouping ratios of 25-75,and 50-50, 75-25, or any other grouping ratio. The percentages of theset of agents in the different groups is not limiting on embodiments ofthe present disclosure and may be determined based on one or morecriteria. For example, the ratio of agents in the groups may bedetermined empirically, and/or may be based at least in part onhistorical data in the one or more databases.

In embodiments, the groupings may be based at least in part on patternperformance ranges. In embodiments, the groupings may be based at leastin part on data volume ranges for the respective agents, e.g., how muchdata is available in the one or more databases for the respectiveagents.

Block 1050 represents an operation of matching for the one group of theagents, by the one or more computers, a respective one of the agentsfrom the one group to one of the calls using a first algorithm. Inembodiments, the matching for the one group of the agents, by the one ormore computers, a respective one of the agents from the one group to oneof the calls may be based at least in part on the performance data forthe pattern of the one call and the performance data of the respectiveone agent in the one group. In embodiments, matching for the one groupof the agents may be based at least in part on the call percentile orranking by performance for the pattern of the one call and the agentpercentile or ranking by performance of the respective one agent in theone group. For example, the matching step for the one group may comprisematching agents in a low percentile or ranking group based at least inpart on the percentile or ranking by performance data for the agents inthe one group and the percentile or ranking by performance data for therespective patterns of the calls. Alternatively, the groups and thematching may be reversed. As noted, in embodiments, the matching mayinstead or in addition be based at least in part on the raw or modifiedperformance data/rating for the agents and/or the raw or modifiedperformance data/rating for the call patterns.

Block 1060 represents an operation of matching for the different groupof the agents, by the one or more computers, a respective one of theagents from the different group of agents to a different one of thecalls using a second algorithm. In embodiments, the matching for thedifferent group of the agents, by the one or more computers, arespective one of the agents from the different group of agents to adifferent one of the calls may be based at least in part on theperformance data for the pattern of the one call and the agentperformance sensitivity to call pattern performance for the one agent inthe different group. In embodiments, the matching for the differentgroup of the agents may be based at least in part on the call percentileor ranking by the performance data for the pattern of the one call andthe agent percentile or ranking by call performance sensitivity for theone agent in the different group. Thus, in embodiments, the matchingstep for the different group may comprise matching agents in a higherpercentile or ranking group based at least in part on performance datafor the call patterns, and on agent performance sensitivity to callperformance in this different group. In embodiments, the matching mayinstead or in addition be based at least in part on the raw or modifiedperformance data/ratings for the call pattern and/or the raw or modifiedsensitivity rating for the agent. Thus, high performance calls are notwasted on agents where call performance has a low influence on theoutcome for the call. These high performance calls are instead matchedwith agents where call performance is expected to have a stronginfluence. In essence, the high performing calls are reserved andmatched to agents where they will be most useful.

In embodiments, the matching steps may be performed concurrently. Inembodiments, the matching steps may be performed consecutively or mayhave overlapping performance. In embodiments, a percentile or rankingbreak point between the one group and the different group may bedetermined based on one or more criteria. As noted, one criterion may beto determine the percentile or ranking cutoff point empirically, and/ormay be based at least in part on historical data in the one or moredatabases or on a level of data availability for the agent of thepattern.

In embodiments, the further operations may be performed of matching, bythe one or more computers, a different set of calls to agents using adifferent matching algorithm. For example, the different algorithm maybe a random matching based at least in part on call position in a queueor length of time of a call in a pool of callers. The operation may beperformed of comparing, performance from call-agent matches of thedifferent set of calls using the different matching algorithm withperformance for the one set of calls using a combination of the matchingof the one group of agents to calls using the first algorithm and thematching of the different group of agents to calls using the secondalgorithm. In embodiments, a further operation may be performed ofgenerating, by the one or more computers, a report or display data forthe performance comparing results from using the combination of matchingalgorithms against performance using the different matching algorithm.

In embodiments, the matching based at least in part on switching betweentwo or more matching algorithms using grouping and based at least inpart on agent sensitivity to call performance may be implemented using asystem as shown in FIG. 14. Referring to the figure, calls are obtained,e.g., either received or generated in a call exchange component 1400. Acall data extractor and pattern generator 1410 may be used to extractcall data from an historical call database 1420 to obtain a call patterncomprising one or more data fields of call data. The historical calldatabase 1420 may include demographic data and/or psychographic data forthe caller, skill requirements for previous calls, and outcomes ofprevious calls, to name a few of the items that may be available. Thiscall data extractor and pattern generator component 1410 may alsoextract call data from other databases and/or generate call data on thefly based on the call number or other call data.

A call pattern performance extractor component 1430 may be connected toreceive the call pattern from the block 1410 and to obtain performancedata for the respective call pattern from the historical call database1420 or to infer it from similar call patterns. In embodiments, the callperformance extractor component 1430 may comprise performing apercentiling or ranking computation operation to percentile or rank thepattern performance for the call against other calls in a set of calls.

Likewise, an agent data and performance extractor component 1450 may beconnected to extract data from an agent database 1440 that containsdemographic data and/or psychographic data and skill data andperformance data for agents. In embodiments, the extractor component1450 may be configured to obtain agent data for agents in a set ofagents, e.g., agents that are currently available, or agents that arecurrently available or are soon to be available, or any other convenientset of agents selected based at least in part on one or more criteriasuch as skill and/or call handle time, to name a few. In embodiments,the agent data and performance extractor component 1450 may perform apercentiling or ranking operation to compute a percentile or to rank theagents within a set of agents based at least in part on performance datafor the agents.

In embodiments, an agent sensitivity to call performance correlationengine 1460 may receive inputs from the call pattern performanceextractor 1430 and from the agent data and performance extractorcomponent 1450, and generate a correlation of the performance for aselected one of the agents to call performance for calls this agent hashandled in the past. In embodiments, this operation may comprise theoperation of computing a percentile or a ranking for the agents in theset of calls by agent performance sensitivity to call performance.

In embodiments, a grouping engine 1470 may be provided to group theagents into two or more groups based on one or more criteria. Inembodiments, the agent grouping may be based at least in part on agentperformance ranges, as previously described. In other embodiments, thegroupings may be based at least in part on volume ranges for agent data,ranges of call handle time, skill level ranges, call performance ranges,to name a few. The grouping engine 1470 is shown in the figure asreceiving inputs from any of blocks 1430, 1450, 1460, and/or potentiallymay also receive an input from block 1410, depending on the one of moreparameters used for the grouping.

In embodiments, a matching engine 1480 using one algorithm may be usedfor one group of agents by matching one of the agents from the one groupof agents to one of the calls in the set of calls based at least in parton the performance data for the pattern of the one call and theperformance data of the respective one agent in the one group. Inembodiments, the matching may be based at least in part on matchingpercentiles or rankings of the call pattern performance within the setof calls and the percentiles or rankings of the agent performance withinthe one group of agents. Block 1480 illustrates an embodiment whereinputs are taken from blocks 1430 and 1450.

In embodiments, a matching engine 1490 using a different algorithm maybe used for an agent in a different group of the agents to a differentcall in the set of calls by selecting based at least in part on theperformance data for the pattern of the one call and based at least inpart on the agent performance sensitivity to call performance for theone agent in the different group. In embodiments, the particularmatching may be agents with high agent sensitivity to call performancematched to calls with high performance data, or any other matchingcriteria that may factor in other data elements such as skill, handletime, call type, etc. In embodiments, the matching may be based at leastin part on matching percentiles or rankings of the agent sensitivity tocall performance within the set of agents and the call performancewithin the set of calls. Block 1490 illustrates an embodiment whereinputs are taken from blocks 1430 and 1460.

In embodiments, a method, system and program product for operating acall center, may comprise combining the agent sensitivity to callperformance and the call pattern sensitivity to agent performance in amatching algorithm. In embodiments, an example may comprise obtainingfor each call in one set of calls, by the one or more computers, arespective pattern representing multiple different respective datafields; obtaining, by the one or more computers, performance data forthe respective patterns of the calls; obtaining, by the one or morecomputers, performance data for the respective agents; determining, bythe one or more computers, agent sensitivity to call performance foragents in a set of agents comprising the agent performance datacorrelated to call performance data for the calls the agent handles;determining, by the one or more computers, pattern performancesensitivity to agent performance comprising the pattern performance datacorrelated to agent performance data; and matching, by the one or morecomputers, a respective one of the agents from the set of agents to oneof the calls based at least in part on the pattern performancesensitivity to agent performance for the respective pattern of the onecall and on the agent sensitivity to call performance for the respectiveone agent of the set of agents.

In embodiments, the same permutations may be used for these embodimentsas are set forth above for other embodiments. For example, the agentsmay be percentiled or ranked based at least in part on their sensitivityto call performance and the calls may be percentiled or ranked based atleast in part on their sensitivity to agent performance, and matchingmay be performed based at least in part on these percentiles orrankings. Likewise, there could be a grouping of the calls and/or of theagents based on one or more criteria, e.g., by performance ranges, orhandle time ranges, or regions or other demographic aspects, to name afew possible grouping criteria. Then different algorithms may be usedfor matching the different groups. For example, matching of calls toagents for one group may be performed based at least in part on callperformance sensitivity to agent performance, and a different group maybe matched based at least in part on agent performance sensitivity tocall performance.

In embodiments, the same weighting algorithms as described above may beused for the agent sensitivity embodiments described above, e.g., theagent performance may be weighted, and/or the call performance may beweighted, and/or the agent performance sensitivity to call performancemay be weighted, and/or the call sensitivity to agent performance may beweighted.

Many of the techniques described here may be implemented in hardware orsoftware, or a combination of the two. Preferably, the techniques areimplemented in computer programs executing on programmable computersthat each includes a processor, a storage medium readable by theprocessor (including volatile and nonvolatile memory and/or storageelements), and suitable input and output devices. Program code isapplied to data entered using an input device to perform the functionsdescribed and to generate output information. The output information isapplied to one or more output devices. Moreover, each program ispreferably implemented in a high level procedural or object-orientedprogramming language to communicate with a computer system. However, theprograms can be implemented in assembly or machine language, if desired.In any case, the language may be a compiled or interpreted language.

Each such computer program is preferably stored on a storage medium ordevice (e.g., CD-ROM, hard disk or magnetic diskette) that is readableby a general or special purpose programmable computer for configuringand operating the computer when the storage medium or device is read bythe computer to perform the procedures described. The system also may beimplemented as a computer-readable storage medium, configured with acomputer program, where the storage medium so configured causes acomputer to operate in a specific and predefined manner.

FIG. 6 illustrates a typical computing system 600 that may be employedto implement processing functionality in embodiments of the presentdisclosure. Computing systems of this type may be used in clients andservers, for example. Those skilled in the relevant art will alsorecognize how to implement embodiments of the present disclosure usingother computer systems or architectures. Computing system 600 mayrepresent, for example, a desktop, laptop or notebook computer,hand-held computing device (PDA, cell phone, palmtop, etc.), mainframe,server, client, or any other type of special or general purposecomputing device as may be desirable or appropriate for a givenapplication or environment. Computing system 600 can include one or moreprocessors, such as a processor 604. Processor 604 can be implementedusing a general or special purpose processing engine such as, forexample, a microprocessor, microcontroller or other control logic. Inthis example, processor 604 is connected to a bus 602 or othercommunication medium.

Computing system 600 can also include a main memory 608, such as randomaccess memory (RAM) or other dynamic memory, for storing information andinstructions to be executed by processor 604. Main memory 608 also maybe used for storing temporary variables or other intermediateinformation during execution of instructions to be executed by processor604. Computing system 600 may likewise include a read only memory(“ROM”) or other static storage device coupled to bus 602 for storingstatic information and instructions for processor 604.

The computing system 600 may also include information storage system610, which may include, for example, a media drive 612 and a removablestorage interface 620. The media drive 612 may include a drive or othermechanism to support fixed or removable storage media, such as a harddisk drive, a floppy disk drive, a magnetic tape drive, an optical diskdrive, a CD or DVD drive (R or RW), or other removable or fixed mediadrive. Storage media 618 may include, for example, a hard disk, floppydisk, magnetic tape, optical disk, CD or DVD, or other fixed orremovable medium that is read by and written to by media drive 612. Asthese examples illustrate, the storage media 618 may include acomputer-readable storage medium having stored therein particularcomputer software or data.

In alternative embodiments, information storage system 610 may includeother similar components for allowing computer programs or otherinstructions or data to be loaded into computing system 600. Suchcomponents may include, for example, a removable storage unit 622 and aninterface 620, such as a program cartridge and cartridge interface, aremovable memory (for example, a flash memory or other removable memorymodule) and memory slot, and other removable storage units 622 andinterfaces 620 that allow software and data to be transferred from theremovable storage unit 618 to computing system 600.

Computing system 600 can also include a communications interface 624.Communications interface 624 can be used to allow software and data tobe transferred between computing system 600 and external devices.Examples of communications interface 624 can include a modem, a networkinterface (such as an Ethernet or other NIC card), a communications port(such as for example, a USB port), a PCMCIA slot and card, etc. Softwareand data transferred via communications interface 624 are in the form ofsignals which can be electronic, electromagnetic, optical or othersignals capable of being received by communications interface 624. Thesesignals are provided to communications interface 624 via a channel 628.This channel 628 may carry signals and may be implemented using awireless medium, wire or cable, fiber optics, or other communicationsmedium. Some examples of a channel include a phone line, a cellularphone link, an RF link, a network interface, a local or wide areanetwork, and other communications channels.

In this document, the terms “computer program product,”“computer-readable medium” and the like may be used generally to referto physical, tangible media such as, for example, memory 608, storagemedia 618, or storage unit 622. These and other forms ofcomputer-readable media may be involved in storing one or moreinstructions for use by processor 604, to cause the processor to performspecified operations. Such instructions, generally referred to as“computer program code” (which may be grouped in the form of computerprograms or other groupings), when executed, enable the computing system600 to perform features or functions of embodiments of the presentdisclosure. Note that the code may directly cause the processor toperform specified operations, be compiled to do so, and/or be combinedwith other software, hardware, and/or firmware elements (e.g., librariesfor performing standard functions) to do so.

In an embodiment where the elements are implemented using software, thesoftware may be stored in a computer-readable medium and loaded intocomputing system 600 using, for example, removable storage media 618,drive 612 or communications interface 624. The control logic (in thisexample, software instructions or computer program code), when executedby the processor 604, causes the processor 604 to perform the functionsof embodiments of the present disclosure as described herein.

It will be appreciated that, for clarity purposes, the above descriptionhas described embodiments of the present disclosure with reference todifferent functional units and processors. However, it will be apparentthat any suitable distribution of functionality between differentfunctional units, processors or domains may be used without detractingfrom the present disclosure. For example, functionality illustrated tobe performed by separate processors or controllers may be performed bythe same processor or controller. Hence, references to specificfunctional units are only to be seen as references to suitable means forproviding the described functionality, rather than indicative of astrict logical or physical structure or organization.

It should be noted that although the flow charts provided herein show aspecific order of method steps, it is understood that the order of thesesteps may differ from what is depicted. Also two or more steps may beperformed concurrently or with partial concurrence. Such variation willdepend on the software and hardware systems chosen and on designerchoice. It is understood that all such variations are within the scopeof the present disclosure. Likewise, software and web implementations ofthe present disclosure could be accomplished with programming techniqueswith rule based logic and other logic to accomplish the various databasesearching steps, correlation steps, comparison steps and decision steps.It should also be noted that the word “component” as used herein and inthe claims is intended to encompass implementations using one or morelines of software code, and/or hardware implementations. It should alsobe noted that the phrase “a plurality” is intended to mean more thanone, and is not intended to refer to any previous recitation of the word“plurality,” unless preceded by the word “the.” When it is stated thatone of A and B, it means that one is selected from the group of A and B.

All components, modes of communication, and/or processes describedheretofore are interchangeable and combinable with similar components,modes of communication, and/or processes disclosed elsewhere in thespecification, unless an express indication is made to the contrary. Itis intended that any structure or step of an embodiment disclosed hereinmay be combined with other structure and or method embodiments disclosedherein to form an embodiment with this added element or step, unless astatement herein explicitly prohibits this combination.

The above-described embodiments of the present disclosure are merelymeant to be illustrative and not limiting. Various changes andmodifications may be made without departing from the present disclosurein its broader aspects. The appended claims encompass such changes andmodifications within the spirit and scope of the present disclosure.

The invention claimed is:
 1. A computer-processor implemented method forpairing in a contact center system comprising: determining, by at leastone computer processor communicatively coupled to the contact centersystem, a pairing strategy based on correlations between agentperformance and contact sensitivity to agent performance; andestablishing, by a routing engine of the contact center system, acommunications channel between a first contact and a first agent basedupon the pairing strategy, wherein the pairing strategy improves overallperformance of the contact center system.
 2. The method of claim 1,wherein the first agent is a high performing agent and the first contacthas high sensitivity to agent performance.
 3. The method of claim 1,wherein the first agent is a low-performing agent, and the first contacthas low sensitivity to agent performance.
 4. The method of claim 1,wherein a successful outcome is predicted for the first contact whenpaired with either a high-performing agent or a low-performing agent. 5.The method of claim 1, wherein an unsuccessful outcome is predicted forthe first contact when paired with either a high-performing agent or alow-performing agent.
 6. The method of claim 1, wherein a successfuloutcome is predicted for the first contact when paired with ahigh-performing agent, and wherein an unsuccessful outcome is predictedfor the first contact when paired with a low-performing agent.
 7. Themethod of claim 1, wherein a higher-performing agent than the firstagent is unlikely to result in a better outcome for the first contactand is reserved for a subsequent pairing with a more influenceablefuture contact.
 8. The method of claim 1, wherein a lower-performingagent than the first agent is likely to result in a worse outcome forthe first contact and is reserved for a subsequent pairing with a lessinfluenceable future contact.
 9. A system for pairing in a contactcenter system comprising: at least one computer processorcommunicatively coupled to the contact center system, wherein the atleast one computer processor is further configured to: determine apairing strategy based on correlations between agent performance andcontact sensitivity to agent performance; and establish, by a routingengine of the contact center system, a communications channel between afirst contact and a first agent based upon the pairing strategy, whereinthe pairing strategy improves overall performance of the contact centersystem.
 10. The system of claim 9, wherein the first agent is a highperforming agent and the first contact has high sensitivity to agentperformance.
 11. The system of claim 9, wherein the first agent is alow-performing agent, and the first contact has low sensitivity to agentperformance.
 12. The system of claim 9, wherein a successful outcome ispredicted for the first contact when paired with either ahigh-performing agent or a low-performing agent.
 13. The system of claim9, wherein an unsuccessful outcome is predicted for the first contactwhen paired with either a high-performing agent or a low-performingagent.
 14. The system of claim 9, wherein a successful outcome ispredicted for the first contact when paired with a high-performingagent, and wherein an unsuccessful outcome is predicted for the firstcontact when paired with a low-performing agent.
 15. The system of claim9, wherein a higher-performing agent than the first agent is unlikely toresult in a better outcome for the first contact and is reserved for asubsequent pairing with a more influenceable future contact.
 16. Thesystem of claim 9, wherein a lower-performing agent than the first agentis likely to result in a worse outcome for the first contact and isreserved for a subsequent pairing with a less influenceable futurecontact.
 17. An article of manufacture for pairing in a contact centersystem comprising: a non-transitory processor readable medium; andinstructions stored on the medium; wherein the instructions areconfigured to be readable from the medium by at least one computerprocessor communicatively coupled to the contact center system andthereby cause the at least one computer processor to operate so as to:determine a pairing strategy based on correlations between agentperformance and contact sensitivity to agent performance; and establish,by a routing engine of the contact center system, a communicationschannel between a first contact and a first agent based upon the pairingstrategy, wherein the pairing strategy improves overall performance ofthe contact center system.
 18. The article of manufacture of claim 17,wherein the first agent is a high performing agent and the first contacthas high sensitivity to agent performance.
 19. The article ofmanufacture of claim 17, wherein the first agent is a low-performingagent, and the first contact has low sensitivity to agent performance.20. The article of manufacture of claim 17, wherein a successful outcomeis predicted for the first contact when paired with either ahigh-performing agent or a low-performing agent.
 21. The article ofmanufacture of claim 17, wherein an unsuccessful outcome is predictedfor the first contact when paired with either a high-performing agent ora low-performing agent.
 22. The article of manufacture of claim 17,wherein a successful outcome is predicted for the first contact whenpaired with a high-performing agent, and wherein an unsuccessful outcomeis predicted for the first contact when paired with a low-performingagent.
 23. The article of manufacture of claim 17, wherein ahigher-performing agent than the first agent is unlikely to result in abetter outcome for the first contact and is reserved for a subsequentpairing with a more influenceable future contact.
 24. The article ofmanufacture of claim 17, wherein a lower-performing agent than the firstagent is likely to result in a worse outcome for the first contact andis reserved for a subsequent pairing with a less influenceable futurecontact.